15 Best Practices for Code Review in Product Engineering Teams

Posted on by Editorial Team

A well-defined code review process within product teams is a powerful enabler for achieving high-quality software and a maintainable codebase. This allows for seamless collaboration among colleagues and an effortless interplay between various engineering disciplines.

With proper code review practices, engineering teams can produce a collaborative culture where learning happens organically, and where improvements to the code commit are welcomed not as a formality but as a step in the agile evolution journey. The importance of code review cannot be understated; however, it can be effectively addressed and underscored within the cyclic approach of the software development life cycle (SDLC) framework. This document seeks to aid teams with the provided recommended best practices to advance their review processes and product quality.

Mindbowser is one of the thought leaders in technology we turned to because they are known for their precise solutions. With years of experience integrating together insights from project work, they learn that quality code always guarantees innovative solutions and assures improved user experience.

Here at ExpertStack, we have developed a tailored list of suggestions which, when followed, enable code authors to maximize the advantages they can gain from participating in the review process. With the implementation of these suggested best practices for code reviews, organizations can cultivate a more structured environment that harnesses workforce collaboration and productive growth.  

In the remaining parts of this article, we will outline best practices to assist code authors serve their submissions to peer reviews and eloquently navigate the complex review process. We’ll provide tried-and-true methods alongside some of our newest strategies, allowing authors to learn the art of submitting reviews and integrating feedback on revisions.

What is the Role of Code Review in Software Development Success?

Enhancing Quality and Identifying Defects

A code review is a crucial step toward fixing bugs and achieving logic error goals in software development. Fixing these issues before a production-level deployment can save software developers a significant amount of money and resources since any bugs will be eliminated before the end users are affected.

Reviewers offer helpful comments which assist in refactoring the code to make it easy to read and maintain. With improved readability comes low-effort comprehensible documentation that can save fellow team members time when maintaining the codebase.

Encouraging sharing and collective learning within teams  

Through code reviews, developers learn different ways of coding and problem-solving which enhances sharing of knowledge within the team. They build upon each other’s understanding, leading to an improvement in the entire team’s proficiency.  

Furthermore, code reviews enable developers to improve their competencies and skills. Learning cultures emerge as a result of team members providing feedback and suggestions. Improvement becomes the norm, and team-wide skills begin to rise.

Identifying and Managing Compliance and Security Risks

Using code reviews to build an organization’s security posture proactively enhances identification and mitigation of security issues and threats in the software development life cycle. In addition, reviews of the software code aid in verifying that the appropriate industry standards were adhered to, thereby certifying that the software fulfills critical privacy and security obligations.

Boosting Productivity in Development Efforts

Through progressive feedback, code reviews are helpful in augmenting productivity in software development by resolving difficulties at the primary stages of development instead of erasing hard-won progress with expensive bug-fixing rounds later on in the project timeline.

Moreover, team members acquire new skills and expertise together through participation in collaborative sessions, making the development team more skilled and productive by enabling them to generate higher-quality code more rapidly thanks to shared skills cultivation.

15 Tips for Creating Code Reviews That Are More Effective

Here are some effective and useful strategies to follow when performing code reviews:

1. Do a Pre-Review Self Assessment

Complete a self-review of the code prior to submission. Fixing simple problems on your own means the reviewer can focus on the more difficult alterations, making the process more productive.

Reviewing changes helps identify oversights, and enables self-optimizing in dealing with a given problem. Utilize code review software like GitHub, Bitbucket, Azure DevOps, or Crucible to aid authors during reviews. These applications let you check the differences between the present version of your code and the most recent one.

These applications let you assess the version that is being compared, where the focus is on changes made. This mindset strengthens evaluation and improvement. Taking the self-review path with advanced recourse aids promotes collaborative and constructive code development and is almost non negotiable for a DevOps culture.

2. Look at the Changes Incrementally  

As review size increases, the value of feedback also decreases in proportion. Conducting reviews across huge swathes of code is quite challenging from both an attention and time perspective; the reviewer is likely to miss detail alongside potential problems. In addition, the risk of review delays may stagnate the work.  

You should try to think of reworking a whole codebase as an iterative process instead. A good example of this is when the code authors submit proposals for new features centered around a module; these can be submitted in the form of smaller review requests for better focus. The advantages of this approach are simply too good to be passed upon.  

The approach provides maximum attention and it becomes much simpler to discover useful feedback. In addition, the work becomes easy and relevant to the developer’s skill level, meaning incorporation becomes much easier. Finally, it reduces the chances of bugs in a simplified modular codebase while paving the way for simpler updates and maintenance down the line.

3. Triage the Interconnected Modifications  

The submission of numerous modifications in a single code review can be overwhelming for the reviewers, making it difficult for them to give detailed and insightful feedback. This type of review exhaustion compounds deconstructive large code reviews with unrelated modifications, providing suboptimal feedback laced with inefficiency.

Nevertheless, addressing this challenge is possible through grouping-related changes. Structuring the modifications by purpose helps in organizing the review to be manageable in scope and focus. Concentrated context enables reviewers to get the required situational awareness, thereby making the feedback more useful and constructive. In addition, concentrated purposive reviews can be easily assimilated into the main codebase thereby facilitating smoother development.

4. Add Explanations

Invest time crafting descriptions by providing precise and comprehensive explanations for the code modifications that are being submitted for review. Commenting or annotating code helps capture its intent, functioning, and the reasoning behind its modifications, aiding reviewers in understanding its purpose.

Following this code review best practice streamlines the code review workflow, improves the overall quality and usefulness of feedback received, and increases engagement rates in regard to code reviews. Interestingly, multiple studies showed that reviewers appreciate a description of the code changes and want people to include descriptions more when requested to submit code for review.

Illustrate the elements simply but provide surrounding context related to the problem or task the changes try to resolve. This provides an impression of the problem resolving the concern. Describe how the modification will resolve the concern and mention how it will impact other components or functions as a cue to flag dependencies or regressions to the reviewers. Add information in regards to other documents, resources, or tickets.

5. Perform Comprehensive Evaluation Tests

For tests, verify your changes to the code with the necessary tests before submitting them for evaluation. It tends to be counterproductive both to the reviewer and the author if broken code is sent for evaluation. Validation of change helps verify if the change is working optimally so that everything is working perfectly. This has resulted in a drop in production defects which is the purpose of  test driven code reviews.

Automated unit tests should be incorporated that will run on their own during the review of the code. Also execute regression tests to confirm the processes functions as required without introducing new problems. For essential parts or changes that are sensitive to performance, do not forget to carry out performance tests in the course of the code review.

6. Automated Code Reviews

In comparison to automated code review, a manual code review may take longer to complete due to human involvement in the evaluation process. In big projects or those with limited manpower, there may be bottlenecks within the code review process. The development timeline might be extended due to unnecessary wait times or red tape.  

Using a tool such as Codegrip for code review automation allows for real-time feedback as well as coherency within the review processes collaboration automation accelerates responses and streamlines reviews. Grade-A automated tools ensure TM-perfection through speed; they check for grade B issues and self-resolve, leaving loopholes for experts to sort the complex grade-A problems.

Using style checkers, automated static analysis tools, and syntax analyzers can improve the quality of the code. This allows you to ensure that reviewers do not spend time commenting on issues that can be resolved automatically, which enables them to provide important insights. In turn, this will simplify the code review process, which fosters more meaningful collaboration between team members.  

Use automated practices which verify compliance with accepted industry standards and internal policies on coding. Use style guidelines specific code formatting software that automatically enforces uniform styling on the code. Add automated verification for defined unit tests triggered during the code review which checks the code change’s functionality.  

Set up Continuous Integration (CI) that uses automated code review processes embedded within the development workflow. CI guarantees that every code change goes through an automated evaluation prior to integration.

7. Fine-Tune Your Code Review Process by Selectively Skipping Reviews

The process of reviewing every single code piece developed by an employee juxtaposes the unique workflow of each company and can quickly gather momentum into a time intensive avalanche of redundancy slamming productivity. Depending on the structure of an organization, skipping certain code reviews may be acceptable. The guideline to disregard code reviews pertains exclusively to trivial alterations that won’t affect any logical operations. These include up-vote comments, basic formatting changes, superficial adjustments, and renaming inline variables.

More significant changes or alterations still require a review to uphold the quality of the code and to guarantee that all concerns are fixed prior to releasing potential hazards.

Set up objectives and rules around the specific criteria that will be established guiding code review bypassing. Use a grade scale to administer a risk-based code review system. Striking a review balance on complicated or pivotal code changes should take precedence over low complexity or straightforward changes. Establish limits or thresholds concerning the scale of modification, impact, or size that will require mandatory code reviews.

Presumably, any minor updates that fall below the designated threshold can be deemed exempt. While having the flexibility not conducting formal reviews, there should always be sufficient counterbalancing measures in place to ensure that there isn’t a steady stream of bypasses resulting in formal review chaos.

8. Optimize Code Reviews Using A Smaller Team Of Reviewers

Choose an optimal number of reviewers based on your code modification. The right number of reviewers is necessary; having too many can be an issue since the review could become disjointed due to little accountability. Too many code reviewers can slow workflow efficiency, communication, and productivity.

Narrowing down the reviewer list to a select few who are knowledgeable fosters precision and agility during the review process without compromising on quality.

Limit participation to those with requisite qualifications as regards the code and the changes undertaken, including knowledge of the codebase. Break down bigger teams into smaller focused teams based on modules or fields of specialization. Focused groups can manage reviews within their designated specialties.

Allow all qualified team members to be lead reviewers but set boundaries that encourage rotation to prevent review burnout. Every team member should be designated to be a lead reviewer at some time. The only role is to plan the review and merge the input for the review.

9. Clarify Expectations

There’s less confusion and better productivity when everyone knows what’s expected in a code review; developers and people reviewing the code are more productive when every aspect of the order is well understood. The overall code review’s effectiveness may be compromised with unclear expectations. Helping reviewers set firm expectations streamlined priority-based task completion and boosted overall speed for the process.

It’s vital to set and communicate expectations before the review begins, such as setting objectives for what a reviewer should achieve beyond simply looking at the code. Along with those goals, set expectations on how long the review would take. Having an estimated range will allow for the boundaries of the review to be set as well as noting which portions of the code are evaluated and which ones need the most focus. 

State if the reviews are scheduled for FP (feature based), sprints, or after important changes are made to code.

Providing review authors and reviewers instruction together with defined objectives aids in reaching common goals around process productivity, along with providing proper guidance towards steps needed to work towards successful completion. Clear guidance on intended outcomes fosters better defined goals for the process which can be shared with all participants leading to sensible improvements and concrete actions, and thereby strengthening outcomes with good suggestions.

10. Add Experienced Reviewers  

The effectiveness of code review is always different due to the knowledge and experience level of the specific reviewers. The review process without experienced reviewers will not be impactful as many crucial details will be missed due to the lack of informed insights. A better rate of recognition of errors improves the standard of code.  

Pick reviewers who have expertise in the area affiliated with the modifications. Have seasoned developers instruct and lead review sessions for junior team members so they learn and improve. Bring senior developers and technical leads for critical and complex reviews so that their insights can be used..  

Allow developers from other teams or different projects to join in on the review process because they will bring a distinct perspective. The inclusion of expert reviewers will permit shifts in the quality of responses given to the developers. Their insights are instrumental as they will tell the developer where vague problems exist, thus enforcing change.

11. Promote Learning

Make sure you involve junior reviewers in the code review process, as it fosters training and learning. Think about putting reviewers who are not familiar with the code to benefit from the review feedback. Code reviews are important from a learning perspective and without some form of motivation are often ignored.

If there is no effort aimed at learning, developers risk overlooking opportunities to gain fresh insights, adopt better industry practices, be more skilled, and advance professionally.

Ask reviewers to give better feedback with useful explanations of industry best practices, alternative methods, and gaps that can be closed. Plan to encourage discussions or presentations about knowledge that needs to be shared. More competent team members can actively mentor the less competent ones.

12. Alert Specific Stakeholders  

Notifying key stakeholders like managers, team members, and team leads regarding the review process helps maintain transparency during development. Often, including too many people in the review notifications causes chaos because reviewers have to waste time figuring out whether the code review is relevant to them.  

Identify stakeholders that need to be notified about the review process and manage expectations as to where reviewers decide whether to notify testers or just provide updates. Utilize tools that allow setting relevant roles for stakeholders and automate notifications via emails or texts.  

Do not send notifications to everyone or scope hands, rather, limit the scope to those who actually benefit from the information at hand.

13. Submit an Advance Request  

Effective scheduling of code reviews helps mitigate any possible bottlenecks in the development workflow. Review requests that are not planned may pose a challenge to reviewers since they may not have ample time to conduct a detailed analysis of the code.

Reviewers receive automatic alerts about the pending reviews well in advance which allocates specific time to their schedules for evaluation. When coding within a large team on intricate features, adjust your calendar for frequent check-in dates.  

Elaborate on the timeframes of the code review to maximize efficiency and eliminate lag time. Investigate if it’s possible to implement review queues. Review queues allow reviewers to select code reviews depending on their schedule. Establish a review structure that increases predictability, benefitting both coders and reviewers.  

Even during the time-sensitive review requests for critical coding that requires priority scrutiny, framework and structure are essential.

14. Accept Reviews to Synergize and Improve Further

Things like additional or different review comments tend to make many people uncomfortable due to how strange they may appear. Teams might become protective and ignore suggestions, which causes blockers to improve efforts.

Accepting feedback with an open mindset allows for code quality change to foster collaboration within the team and culture improves over time. Code feedback acceptance positivity by teams lead to increase in morale and job satisfaction as well as 20% code quality improvement which was noticed by one researcher.

Stay open to reviewer suggestions plus their reasoning, and to the points they put forth because they are worth dropping attempts to increase the code quality instead. Talk to reviewers about their suggestions or comments with the aim of clarification where needed. 

Assist reviewers to sustain coded quality of their feedback and seek suggestions from impacted individuals to actively look to make posed suggestive change result maintaining high as gratitude.

15. Thank Contributors for In-Depth Review of Code Critiques

Reviewers often feel demotivated for putting time into the review and feedback process. If appreciated, it motivates them to continue engaging with the review process. Expressing thanks to reviewers not only motivates them but also helps cultivate a positive culture and willingness to engage with feedback.

Concisely, express thanks in team meetings to the respective reviewers or send a dedicated thank you to the group. Inform all of the team members to notify the reviewers on the feedback implementation after the actions and decisions are made regarding the feedback. As a form of gratitude for their hard work, periodically award small tokens of appreciation to the reviewers.

A $41,200 humanoid robot was unveiled in China

Posted on by Editorial Team

The Chinese company UBTech Robotics presented a humanoid robot for 299,000 yuan ($41,200). This is reported by SCMP.

Tien Kung Xingzhe was developed in collaboration with the Beijing Humanoid Robot Innovation Center. It is available for pre-order, with deliveries expected in the second quarter.

The robot is 1.7 meters tall and can move at speeds of up to 10 km/h. Tien Kung Xingzhe easily adapts to a variety of surfaces, from slopes and stairs to sand and snow, maintaining smooth movements and ensuring stability in the event of collisions and external interference.

The robot is designed for research tasks that require increased strength and stability. It is powered by the new Huisi Kaiwu system from X-Humanoid. The center was founded in 2023 by UBTech and several organizations, including Xiaomi. He develops products and applications for humanoids.

UBTech’s device is a step towards making humanoid robots cheaper, SCMP notes. Unitree Robotics previously attracted public attention by offering a 1.8-meter version of the H1 for 650,000 yuan ($89,500). These robots performed folk dances during the Lunar New Year broadcast on China Central Television in January.

EngineAI’s PM01 model sells for 88,000 yuan ($12,000), but it is 1.38 meters tall. Another bipedal version, the SA01, sells for $5,400, but without the upper body.

In June 2024, Elon Musk said that Optimus humanoid robots will bring Tesla’s market capitalization to $25 trillion.

Elon Musk Blames ‘Massive Cyber-Attack’ for X Outages, Alleges Ukrainian Involvement

Posted on by Editorial Team

Elon Musk has claimed that a “massive cyber-attack” was responsible for widespread outages on X, the social media platform formerly known as Twitter. The billionaire suggested that the attack may have been orchestrated by a well-resourced group or even a nation-state, potentially originating from Ukraine.

X Faces Hours of Service Disruptions
Throughout Monday, X experienced intermittent service disruptions, preventing users from loading posts. Downdetector, a service that tracks online outages, recorded thousands of reports, with an initial surge around 5:45 AM, followed by a brief recovery before another wave of disruptions later in the day. The majority of issues were reported on the platform’s mobile app.

Users attempting to load tweets were met with an error message reading, “Something went wrong,” prompting them to reload the page.

Musk addressed the situation in a post on X, stating:

We get attacked every day, but this was done with a lot of resources. Either a large, coordinated group and/or a country is involved.”

However, Musk did not provide concrete evidence to support his claims.

Musk Suggests Ukrainian Involvement
Later in the day, during an interview with Fox Business, Musk doubled down on his allegations, suggesting that the attack may have originated from Ukraine.

“We’re not sure exactly what happened, but there was a massive cyber-attack to try and bring down the X system with IP addresses originating in the Ukraine area,” Musk stated.

The claim comes amid Musk’s increasingly strained relationship with the Ukrainian government. Over the weekend, he asserted that Ukraine’s “entire front line” would collapse without access to his Starlink satellite communication service. Additionally, he criticized U.S. Senator Mark Kelly, a supporter of continued aid to Ukraine, labelling him a “traitor.”

A Pattern of Unverified Cyber-Attack Claims
Musk has previously attributed X outages to cyber-attacks. When his live-streamed interview with Donald Trump crashed last year, he initially claimed it was due to a “massive DDoS attack.” However, a source later told The Verge that no such attack had occurred.

Broader Challenges for Musk’s Businesses
The disruptions at X add to a series of recent setbacks for Musk’s ventures.

SpaceX Mishap: On Friday, a SpaceX rocket exploded mid-flight, scattering debris near the Bahamas.
Tesla Under Pressure: A growing “Tesla takedown” movement has led to protests at dealerships, while Tesla’s stock price continues to slide, hitting its lowest point in months.
Political Tensions: Musk’s meeting with Donald Trump last week reportedly grew tense, with Trump hinting at curbing the billionaire’s influence over government agencies.

The Bottom Line
While Musk attributes X’s outages to a large-scale cyber-attack, no independent evidence has surfaced to confirm this claim. Given his history of making similar allegations without substantiation, the true cause of the disruption remains unclear. Meanwhile, mounting challenges across Musk’s business empire suggest that cyber-attacks may not be the only crisis he is facing.

The Role of Digital Twins in Building the Next Generation of Data Centers

Posted on by Editorial Team 
contributed by Aleksandr Karavanin, Production Engineer at Meta

With increasing numbers of new-age businesses relying on online services, data centers have become the backbone of global operations. However, it has become increasingly difficult to maintain them, with complexities such as power efficiency, downtime of systems, and real-time monitoring. In an effort to address these problems, Digital Twin technology has become a game-saver, which allows organizations to create virtual representations of their data centers to achieve maximum performance, predict failures, and improve operational efficiency.

Understanding Digital Twins in Data Centers

A Digital Twin  is a virtual representation of a physical system, continuously updated with real-time data to reflect the actual conditions of the infrastructure. For data centers, digital twins merge Internet of Things (IoT) sensors, Artificial Intelligence (AI), and machine learning algorithms to monitor and replicate real world conditions in authentic-to-life depiction.

Data center management has moved from manual monitoring and reactive maintenance to AI-driven automation. The transition enables IT teams to make data-driven decisions to achieve maximum resource utilization, zero downtime, and improved performance.

One of the greatest advantages of digital twins is that they provide real-time insight into data center operations. By constantly consuming data from power usage, cooling systems, and hardware performance, the virtual replicas provide a comprehensive view of the health of the facility. Virtual simulations allow organizations to experiment with different configurations, optimizing energy efficiency and reducing operational risks.

On the other hand, one of the key benefits of digital twins is their ability to enable proactive decision-making through real-time monitoring. By continuously analyzing incoming data from critical systems, digital twins offer IT teams unparalleled visibility into the health and efficiency of the data center.

Benefits of Real-Time Monitoring:

Real-time monitoring is a crucial aspect of data center management, ensuring efficiency and preventing interruptions. Digital twins provide a real-time flow of information from various infrastructure components, allowing IT personnel to detect inefficiencies, predict resource needs, and solve potential issues ahead of time. Leveraging this real-time visibility, organizations can enhance performance and reduce operational risks.

  • Faster Issue Detection and Troubleshooting

Digital twins enable IT personnel to identify and fix system failures before they are an issue. By constantly monitoring cooling system data, power usage, and server performance, they trigger instantaneous alerts when issues are detected, allowing for immediate response.

  • Increased Capacity Planning

By analyzing data trends, organizations are able to predict when additional resources will be required, scaling seamlessly. It helps businesses scale their data center operations in a cost-effective way, preventing bottlenecks and optimizing resource utilization.

These benefits are not just theoretical, leading tech companies are already leveraging digital twins to transform their data center operations. One standout example is Thésée DataCenter, which has successfully implemented digital twin models to optimize its cooling systems.

Thésée DataCenter opened the first fully interactive digital twin in a colocation environment in 2022. The digital twin provides customers with a 3D view of their IT equipment, power usage, and operating conditions, with real-time visibility on performance and service levels. By enabling precise knowledge of infrastructure capacity and risk-free planning of future installations, Thésée DataCenter has simplified capacity planning and anticipated necessary changes to cooling infrastructure, achieving aggressive energy performance objectives.

Apart from real-time monitoring and capacity planning, digital twins also play a critical part in predictive maintenance and proactive incident management. Rather than addressing issues after they happen, digital twin technology allows organizations to shift from a reactive to a predictive maintenance approach, reducing the likelihood of surprise failures.

Predictive Maintenance and Proactive Incident Response

Traditional data center maintenance often follows a reactive approach, addressing issues only after they cause disruptions. Digital twins, however, enable a shift toward predictive maintenance, where AI-driven analytics detect potential failures before they occur.

By analyzing historical and real-time data, digital twins identify patterns that indicate impending hardware failures or cooling inefficiencies. This predictive capability reduces the risk of sudden outages, minimizing downtime and repair costs.

Beyond predicting failures, digital twins also enhance proactive incident response, it is a crucial advantage of digital twin technology in data center management.Through AI-based automation and real-time analytics, digital twins allow organizations to detect possible risks early and respond instantly, minimizing disruptions and ensuring continuity of operations.

Automated Risk Detection

AI constantly monitors hardware performance, power fluctuations, and security threats, analyzing massive amounts of information in real-time. Preemptive monitoring enables IT personnel to identify anomalies that can predict impending failures, such as overheated servers, power supply irregularities, or attempts at unauthorized entry. By detecting these threats before they occur, organizations prevent cascading failures that can trigger downtime or security incidents.

For example, if a digital twin detects unusual power consumption in a server rack, it can warn of a potential power supply issue before it results in an outage. Similarly, in security scenarios, AI-driven monitoring can flag suspicious access patterns, enabling IT personnel to take action before a security breach occurs.

However, detecting anomalies is only the first step, timely alerts and swift response mechanisms are equally critical to preventing disruptions. This is where AI-driven alerts come into play, ensuring that IT teams receive real-time notifications and can take immediate corrective action.

AI-Driven Alerts and Immediate Response

Digital twins not only detect issues but also generate automated alerts based on predefined thresholds and AI-driven insights. These alerts provide IT teams with real-time notifications about potential risks, enabling them to take immediate corrective action.

  • Real-Time Notifications: Digital twins send instant alerts through dashboards, emails, or integrated management systems, ensuring IT personnel are informed the moment an issue arises.
  • Automated Mitigation Actions: In some cases, AI can trigger automated responses, such as redistributing workloads to prevent overheating, adjusting cooling parameters, or isolating compromised systems to mitigate security threats.
  • Incident Prioritization: By analyzing the severity of detected issues, digital twins help IT teams prioritize responses, ensuring critical problems are addressed first while routine maintenance tasks are scheduled accordingly.

This proactive approach reduces downtime, optimizes resource utilization, and enhances the overall resilience of data center operations. But how effective is this in practice?

A premier cloud services company leveraged digital twin technology to improve data center reliability and reduce operational costs, resolving for unexpected server failures that caused costly downtime and increased maintenance costs. By integrating digital twins in its infrastructure, the company created virtual replicas of its physical servers, cooling systems, and power distribution networks that allowed real-time monitoring of the critical parameters such as CPU temperature, workload balancing, power fluctuations, airflow efficiency, and security threats. 

With AI-powered predictive analytics, the digital twin picked up early warning signs of the potential failures before they had turned into critical problems. This deployment caused a reduction of 30% in downtime, with AI detecting anomalies in server performance, triggering real-time alerts and enabling IT teams to replace or repair components before disruption. Automated mitigation strategies, such as workload redistribution, also ensured continued service continuity. 

Predictive maintenance also lowered maintenance costs by 20%, with fewer emergency repairs, optimized scheduling of routine maintenance, and improved efficiency of cooling systems to lower energy consumption. The enhanced monitoring and proactive incident response also raised service reliability, allowing IT teams to divert their energies away from reactive problem-solving and towards strategic innovation, and ultimately, improving uptime and customer satisfaction. 

In this change, the cloud services provider demonstrated how AI-driven predictive analytics and digital twins can significantly enhance infrastructure resilience and cost efficiency.

Future of Digital Twins in Data Centers

The previous case study highlights the huge benefits of AI-driven digital twins in enhancing data center operations. As we can see, the use of digital twin technology has led to stunning decreases in downtime, maintenance costs, and overall improvements in service reliability. These advantages highlight the huge potential digital twins hold to transform data centers today. Looking ahead, the future of digital twins in data centers seems even more promising.

As AI and machine learning continue to advance, the capabilities of digital twins will expand, offering even greater automation and efficiency in data center operations. The rapid integration of edge computing and high speed mobile networks will further enhance real-time data processing, enabling faster decision-making and improved latency management.

However, the widespread adoption of digital twins is not without challenges. Data security concerns, high implementation costs, and system complexity remain potential obstacles. Consequently, organizations must ensure robust cybersecurity measures and assess the return on investment before deploying digital twin solutions at scale.

Conclusion

In conclusion, digital twins are transforming data center management by enabling real-time simulation, predictive maintenance, and proactive incident response. As organizations strive for smarter, self-optimizing and self-healing data centers, digital twin technology will play a crucial role in ensuring efficiency, reliability, and sustainability.

Looking ahead, businesses that embrace digital twins will gain a competitive advantage, reducing operational risks and improving resource management. Finally, as technology evolves, the future of data centers will be defined by intelligent automation, setting the stage for a new era of digital infrastructure.

How scam HR can run virus on your PC

Posted on by Editorial Team

Imagine getting an offer for your dream job, but handing over your computer to a hacker in the process.

This isn’t a plot from a cybersecurity thriller. It’s the reality of a growing threat in the digital recruitment space, where job scams have evolved from phishing emails to full-blown remote code execution attacks disguised as technical assessments. We invited Akim Mamedov, a CTO to share his experience and recommendations.

***

For quite some time there were rumors that a new type of scam emerged in hiring, especially in platforms like LinkedIn. I didn’t pay enough attention until I encountered this scam scheme personally.

The truth is that almost every scam relies on social engineering, e.g., to lure a person in performing some action without paying enough attention. This kind is similar, the desired outcome is running malicious code on the user ‘s computer. Now let’s dive deep in the details and explore how the schema works and how bad guys attempt to do their dirty business.

After surfing on LinkedIn I’ve received a message from the guy about an interesting job offer. He described the role in detail, promised a good salary and was actively asking for my attention.

Before switching to Telegram I checked the profile of the guy and it looked pretty decent – good working experience, extensive profile information, linked university and company where he supposedly works.

After proceeding to telegram we decided to schedule a call.

On the call, I had a chance to see him in person – it was an Indian guy with a long beard. I hadn’t opportunity to take screenshots because he immediately turned his camera off. This is when it started to look suspicious as hell so I’ve started making screenshots of everything.

He asked a couple of quick questions like tell me about a project and confirm that you’ve worked with this and with that. At the end of the call he said that there is still a small test task which I have to solve and then they will hire me.

That’s where the interesting part begins. I’ve opened the archive and started checking the code.

Meanwhile I’ve messaged a couple of questions to HR so he got the feeling that i’m aware about the malware and deleted messages in telegram and linkedin. Now let’s focus on what the code does.

From the first glance, it’s a simple javascript backend project.

But what @el3um4s/run-vbs and python-shell does inside this simple js test task?

After quick search of usages i’ve found a file where this package is actually used

There are 2 files – one for Windows OS and the other for any other OS with python installed. Let’s check one with python code.

Inside the file with python code we have a script which collects some computer information and sends it to the server. Response from that server could contain instructions which go directly to the exec() function thus executing arbitrary code in the system. This looks like a botnet script which keeps an endless connection to the attacker server and waits until the server responds to perform some actions. Needless to say that running this script means passing your system to an attacker thus allowing reading sensitive data, tinkering with OS services and utilizing computer resources.

This is the opinion of ChatGPT regarding the code in that file.

The impact of this scheme could possibly be big enough to infect thousands of computers. Sure there are a lot of arrogant developers who consider this test task too easy for spending more than a couple of minutes and will try to finish it fast. Junior developers are at risk too – lured with high salaries and non-demanding job descriptions, they will run the project without properly understanding it.

In conclusion, be mindful of the code you’re trying to run, always check any source code and script you’re running.


Unleashing Powerful Analytics: Harnessing Cassandra with Spark

Posted on by Editorial Team 
Authored by Abhinav Jain, Senior Software Engineer

The adoption of Apache Cassandra and Apache Spark is a game-changer for organizations seeking to change their analytics capabilities in the modern world driven by data. With its decentralized architecture, Apache Cassandra is highly effective in dealing with huge amounts of data while ensuring low downtime. This occurs across different data centers which can be said as well for both fault tolerance and linear scalability: the reason why more than 1,500 companies — such as Netflix and Apple — deploy Cassandra. On the other hand, Apache Spark further boosts this system by processing data in memory, allowing speeds up to 100 times faster than disk-based systems and greatly enhancing the setup introduced by Cassandra.

A fusion of Cassandra and Spark results in not just a speedup, but an improvement of data analytics quality. The organizations that use this report drastically decrease their data processing time from hours to minutes — vital for finding insights quickly. This has brought them closer to staying ahead in the competitive markets since the two technologies work well together: When used jointly, Spark and Cassandra are best suited for real-time trend analysis.

On top of that, the integration of these two technologies is proposed as a response to the growing demand for flexible and scalable solutions in areas as broad as finance, where integrity, validity and speed play an important role. This coaction helps organizations not only control larger sets more expediently but also find valuable intelligence with a pragmatic approach: the decision is made based on their operation or the strategic move of their business. Given this, it becomes evident that knowledge about Cassandra’s integration with Spark should be part of every organization that intends to improve its operational analytical data.

Preface: Combining Cassandra’s Distribution with Spark’s In-Memory Processing

The use of Apache Cassandra has been a common choice for organizations that have large volumes of data to manage since they need distributed storage and handling capabilities. However, its decentralized architecture and tunable consistency levels — along with the ability to distribute large amounts of data across multiple nodes — is what makes it ideal without introducing minimal delays. In contrast, Apache Spark can work out processing and analyzing data in memory, which complements Cassandra as an outstanding partner able to deliver real-time analytics plus batch processing tasks.

Setting Up the Environment

To optimally prepare the environment for analytics using Cassandra and Spark, you start the process by installing Apache Cassandra first, then launching a Spark cluster. Both components need individual attention during configuration to promote harmony and achieve the best output from each side. The inclusion of connectors like DataStax Spark Cassandra Connector or Apache Spark Cassandra Connector is pivotal, since they help in effective data flow between Spark and Cassandra systems. Such connectors enhance query operation through Spark’s easy access to data from Cassandra without much network overhead due to parallelism optimization.

With the connectors having been configured, it’s equally vital that you tinker with the settings in a bespoke manner to cater to the workload specifics and volume of data. This could entail tweaking Cassandra’s compaction strategies and Spark’s memory management configurations — adjustments that must be made with anticipation of the incoming data load. The last leg of this journey is verifying the setup through test data: the successful integration signals workability, enabling a seamless analytics operation with due expectations. This setup — robust and intricate — acts as a fulcrum for both technologies, allowing them to be used at full capacity in one coherent analytics environment.

Performing Analytics with Spark and Cassandra

A fusion of Spark with Cassandra results in an enhancement of data processing: through the utilization of Spark’s efficient distribution model and Cassandra’s powerful computing capabilities. The end users are therefore able to perform advanced queries and deal with large datasets easily using Cassandara’s direct storage framework. In addition, these capabilities are enhanced by a number of libraries embedded within Spark, such as MLlib for machine learning, GraphX for graph processing, and Spark SQL for structured data handling — tools that support easy execution of complex transformations, and predictive analytics and data aggregation tasks. Furthermore, by caching data in memory, Spark speeds up iterative algorithms and queries, thus making it ideal where frequent data access is needed, coupled with manipulation via an intuitive user interface. The integration improves workflow and maintains high performance even after scaling to meet growing demands on big data across landscapes where large amounts prevail.

Real-time Analytics and Stream Processing

Furthermore, Spark plus Cassandra real-time analytics is a good approach to organizations’ intake and immediate analysis of data flows. This value is especially important for the business where speed and informativity are important. For example, monitoring of financial transactions, social network activity or IoT output information. Through Spark Streaming, data can be ingested in micro-batches and processed continuously with the possibility of implementing complex algorithms on the fly. When Spark is used with the CDC feature from Cassandra or tightly integrated with Apache Kafka as part of message queuing infrastructure, it turns into a powerful weapon that allows development teams to craft feedback-driven analytical solutions supporting dynamic decision processes which adapt towards changes unearthed from incoming data streams.

Machine Learning and Advanced Analytics

In addition to traditional analytics tasks, Spark opens up possibilities for advanced analytics and machine learning with Cassandra data. Users can create and model machine learning from Cassandra-stored data without having to move or duplicate it, hence enabling predictive analytics and anomaly detection as well as other high-end use cases through the adoption of Spark’s MLlib plus ML packages.

Best Practices and Considerations

One must take into account the best practices when integrating Spark and Cassandra for advanced analytics so that their potential can be maximized effectively. To ensure this, it is important to modify the data model of Cassandra in a way that meets the query patterns, helping reduce read and write latencies. In addition, when using partition keys design, distribute data equally across nodes to prevent hotspots while also configuring Spark’s memory and core settings appropriately. This will help you avoid resource overcommitment and thus any unnecessary performance issues.

Moreover, monitoring of both Spark and Cassandra clusters should be maintained continuously. Make use of tools such as Apache Spark’s web UI and Cassandra’s nodetool that can help you with performance metrics which would lead to bottlenecks showing up in no time. You must put in place strict data governance policies; this involves carrying out regular audits and compliance checks, which would ensure data integrity and security. Ensure secure access to data using authentication plus encryption (both in transit and at rest) that prevents unauthorized access and breaches.

Conclusion

Combining Apache Cassandra and Apache Spark creates a significant platform for large-scale analytics: it helps organizations get valuable and meaningful data much quicker than they ever did. By taking advantage of what each technology does best, companies have the opportunity to stay ahead of the competition, foster innovation, and ensure their decisions are based on quality data. Be it historical data analysis, streaming data processing as it flows or constructing machine learning frameworks, Cassandra and Spark, when brought together, form an adaptable and expandable solution for all your analytical needs. 

Observability at Scale

Posted on by Editorial Team 
Authored by Muhammad Ahmad Saeed, Software Engineer

This article has been carefully vetted by our Editorial Team, undergoing a thorough moderation process that includes expert evaluation and fact-checking to ensure accuracy, and reliability.

***

In today’s digital world, businesses operate on complex, large scale systems designed to handle millions of users simultaneously. What is the challenge one might wonder? Keeping these systems reliable, performant, and user friendly at all times. For organizations that rely on microservices, distributed architectures, or cloud native solutions, downtime can have disastrous consequences.

This is where observability becomes a game changer. Unlike traditional monitoring, which focuses on alerting and basic metrics, observability offers a deeper understanding of system behavior by providing actionable insights from the system’s output. It empowers teams to diagnose, troubleshoot, and optimize systems in real time, even at scale. When it comes to engineers, observability isn’t just a tool for them , it’s rather a lifeline for navigating the complexity of modern infrastructure.

What Is Observability?

Observability is the ability to deduce the internal states of a system by analyzing the data it produces during operation. This concept, originally derived from control theory, which focuses on the principle that a system’s behavior and performance can be understood, diagnosed, and optimized without directly inspecting its internal mechanisms. In the realm of modern software engineering, observability has transformed into a foundational practice for managing complex, distributed systems. In order to fully understand observability, let’s unpack its three pillars:

  1. Logs: Logs are immutable, time stamped records of events within your system. They help capture context when errors occur or when analyzing specific events. For example, a failed login attempt might produce a log entry with details about the request.
  2. Metrics: Metrics are quantitative measurements that indicate system health and performance. Examples include CPU usage, memory consumption, and request latency. These metrics are great for spotting trends and anomalies.
  3. Traces: Traces map the journey of a request through a system. They show how services interact and highlight bottlenecks or failures. Tracing is especially valuable in microservices environments, where a single request can touch dozens of services.

Collectively, these components provide a view of the entire behavior of a system, making it possible for teams to be able to address important questions, such as why a certain service is slower than it should be, what triggered an unexpected rise in errors, and whether certain identifiable patterns have led up to system failures.

While observability can significantly improve reliability, achieving it at scale presents some  challenges. Since as systems grow in size and complexity, so does the volume of data they generate. Therefore, managing and interpreting this data effectively requires robust strategies and tools to address several key challenges, some of which are presented next.

One major hurdle is the massive volume of data produced by large scale systems. Logs, metrics, and traces accumulate rapidly, creating significant demands on storage and processing resources. Without efficient aggregation and storage strategies, organizations risk escalating costs while making it increasingly difficult to extract meaningful insights.

Another challenge arises from context loss in distributed systems. In modern architectures like microservices, a single request often traverses numerous services, each contributing a piece of the overall workflow. If context is lost at any point, whether due to incomplete traces or missing metadata, debugging becomes an error prone task. 

Finally, distinguishing the signal from the noise is a persistent problem. Not all data is equally valuable, and the sheer quantity of information can obscure actionable insights. Also, advanced filtering, prioritization techniques, and intelligent alerting systems are essential for identifying critical issues without being overwhelmed by less relevant data.

Addressing these challenges requires both technological innovation and thoughtful system design, ensuring observability efforts remain scalable, actionable, and cost effective as systems continue to evolve. Let’s take Netflix as an example, which streams billions of hours of content to users worldwide. Their system comprises thousands of microservices, each contributing logs and metrics, so without a robust observability strategy, pinpointing why a particular user is experiencing buffering would be nearly impossible. This streaming platform overcomes this by using tools like Atlas (their in-house monitoring platform) to aggregate, analyze, and visualize data in real time.

Best Practices for Achieving Observability at Scale

As modern systems grow increasingly complex and distributed, achieving effective observability becomes critical for maintaining performance and reliability. However, scaling observability requires more than just tools, it actually demands strategic planning and best practices. Below, we explore five key approaches to building and sustaining observability in large scale environments.

  1. Implement Distributed Tracing
     Distributed tracing tracks requests as they flow through multiple services, allowing teams to pinpoint bottlenecks or failures. Tools such as OpenTelemetry and Zipkin make this process seamless.
  2. Use AI-Powered Observability Tools
     At scale, manual monitoring becomes impractical. AI-driven tools like Datadog and Dynatrace use machine learning to detect anomalies, automate alerting, and even predict potential failures based on historical patterns. 
  3. Centralize Your Data
     A fragmented observability approach where logs, metrics, and traces are stored in separate silos, leads to inefficiencies and miscommunication. However, centralized platforms like Elastic Stack or Splunk enable teams to consolidate data and access unified dashboards.
  4. Adopt Efficient Data Strategies
     Realistically, collecting and storing every piece of data is neither cost effective nor practical. The best approach is to implement data sampling and retention policies to store only the most relevant data, ensuring scalability and cost optimization.
  5. Design for Observability from the Start
     Observability shouldn’t be an afterthought. It is best to build systems with observability in mind by standardizing logging formats, embedding trace IDs in logs, and designing APIs that expose meaningful metrics.

To sum up, observability at scale is not just a good-to-have but an absolute must have in today’s fast moving and complex technical environment. Organizations will be able to ensure seamless performance and rapid problem resolution by following best practices like distributed tracing, AI-powered tooling, centralization of data, efficient strategies, and designing systems for observability. 

The Business Benefits of Observability

Although the journey to robust observability is not easy, improvements in reliability, decreased debugging time, and a better user experience are priceless. Besides the key approaches tackled above, there is also effective observability that extends far beyond technical gains, where it has measurable impacts on business outcomes:

  • Reduced Downtime: Proactive issue detection minimizes the time systems remain offline, saving millions in potential revenue loss.
  • Faster Incident Resolution: Observability tools empower teams to identify and fix issues quickly, reducing mean time to resolution (MTTR).
  • Better User Experience: Reliable, responsive systems enhance user satisfaction and retention.

For example, Slack, the widely used messaging platform, leverages observability to maintain its 99.99% uptime and ensure seamless communication for businesses worldwide. By implementing automated incident detection and proactive monitoring, Slack can identify and address issues in real time, minimizing disruptions. Their resilient microservices architecture further contributes to maintaining reliability and uptime.

Conclusion: 

To conclude, in an era defined by ever evolving large scale systems, observability has shifted from being a luxury to a necessity. Teams must deeply understand their systems to proactively tackle challenges, optimize performance, and meet user expectations. Through practices like distributed tracing, AI-driven analytics, centralized data strategies, and designing systems for observability from the ground up, organizations can transform operational chaos into clarity.

However, the true value of observability extends beyond uptime or issue resolution. It represents a paradigm shift in how businesses interact with technology, offering confidence in infrastructure, fostering innovation, and ultimately enabling seamless scalability. As technology is constantly evolving, the question is no longer whether observability is necessary, but whether organizations are prepared to harness its full potential. 

From Bugs to Brilliance: How to Leverage AI to Left-Shift Quality in Software Development

Posted on by Editorial Team 

Contributed by Gunjan Agarwal, Software Engineering Manager at Meta
Key Points
  • Research suggests AI can significantly enhance left-shifting quality in software development by detecting bugs early, reducing costs, and improving code quality.
  • AI tools like CodeRabbit and Diffblue Cover have proven effective in automating code reviews and unit testing, significantly improving speed and accuracy in software development.
  • The evidence leans toward early bug detection, saving costs, with studies showing fixing bugs in production can cost 30-60 times more than early stages.
  • An unexpected detail is that AI-driven CI/CD tools, like Harness, can reduce deployment failures by up to 70%, enhancing release efficiency.

Introduction to Left-Shifting Quality

Left-shifting quality in software development involves integrating quality assurance (QA) activities, such as testing, code review, and vulnerability detection, earlier in the software development lifecycle (SDLC). Traditionally, these tasks were deferred to the testing or deployment phases, often leading to higher costs and delays due to late bug detection. By moving QA tasks to the design, coding, and initial testing phases, teams can identify and resolve issues proactively, preventing them from escalating into costly problems. For example, catching a bug during the design phase might cost a fraction of what it would cost to fix in production, as evidenced by a study by the National Institute of Standards and Technology (NIST), which found that resolving defects in production can cost 30 to 60 times more, especially for security defects.

The integration of artificial intelligence (AI) into this process has been able to left-shifting quality, offering automated, intelligent solutions that enhance efficiency and accuracy. AI tools can analyze code, predict failures, and automate testing, enabling teams to deliver high-quality software faster and more cost-effectively. This article explores the concept, benefits, and specific AI-powered techniques, supported by case studies and quantitative data, to provide a comprehensive understanding of how AI is transforming software development.

What is Left-Shifting Quality in Software Development?

Left-shifting quality refers to the practice of integrating quality assurance (QA) processes earlier in the software development life cycle (SDLC), encompassing stages like design, coding, and initial testing, rather than postponing them until the later testing or deployment phases. This approach aligns with agile and DevOps methodologies, which emphasize continuous integration and delivery (CI/CD). By conducting tests early, teams can identify and address bugs and issues before they become entrenched in the codebase, thereby minimizing the need for extensive rework in subsequent stages.​

The financial implications of detecting defects at various stages of development are significant. For example, IBM’s Systems Sciences Institute reported that fixing a bug discovered during implementation costs approximately six times more than addressing it during the design phase. Moreover, errors found after product release can be four to five times more expensive to fix than those identified during design, and up to 100 times more costly than errors detected during the maintenance phase. ​

This substantial increase in cost underscores the critical importance of early detection. Artificial intelligence (AI) facilitates this proactive approach through automation and predictive analytics, enabling teams to identify potential issues swiftly and accurately, thereby enhancing overall software quality and reducing development costs.​

Benefits of Left-Shifting with AI

The benefits of left-shifting quality are significant, particularly when enhanced by AI, and are supported by quantitative data:

  • Early Bug Detection: Research consistently shows that addressing bugs early in the development process is significantly less costly than fixing them post-production. For instance, a 2022 report by the Consortium for Information & Software Quality (CISQ) found that software quality issues cost the U.S. economy an estimated $2.41 trillion, highlighting the immense financial impact of unresolved software defects. AI tools, by automating detection, can significantly reduce these costs.​
  • Faster Development Cycles: Identifying issues early allows developers to make quick corrections, speeding up release cycles. For example, AI-driven CI/CD tools like Harness have been shown to reduce deployment time by 50%, enabling faster iterations Harness Case Study.
  • Improved Code Quality: Regular quality checks at each stage, facilitated by AI, reinforce best practices and promote a culture of quality. Tools like CodeRabbit reduce code review time, improving developer productivity and code standards.​
  • Cost Savings: The financial implications of software bugs are profound. For instance, in July 2024, a faulty software update from cybersecurity firm CrowdStrike led to a global outage, causing Delta Air Lines to cancel 7,000 flights over five days, affecting 1.3 million customers, and resulting in losses exceeding $500 million. AI-driven early detection and remediation can help prevent such costly incidents.​
  • Qualitative Improvements:Developer Well-being: AI tools like GitHub Copilot have shown potential to support developer well-being by improving productivity and reducing repetitive tasks – benefits that some studies link to increased job satisfaction. However, evidence on this front remains mixed. Other research points to potential downsides, such as increased cognitive load when debugging AI-generated code, concerns over long-term skill degradation, and even heightened frustration among developers. These conflicting findings highlight the need for more comprehensive, long-term studies on AI’s true impact on developer experience.

Incorporating AI into software development processes offers significant advantages, but it’s crucial to balance these with an awareness of the potential challenges to fully realize its benefits.

AI-Powered Left-Shifting Techniques

AI offers a suite of techniques that enhance left-shifting quality, each addressing specific aspects of the SDLC. Below, we detail six key methods, supported by examples and data, explaining their internal workings, the challenges they face, and their impact on reducing cognitive load for developers.

1. Intelligent Code Review and Quality Analysis

Intelligent code review tools use AI to analyze code for quality, readability, and adherence to best practices, detecting issues like bugs, security vulnerabilities, and inefficiencies. Tools like CodeRabbit employ large language models (LLMs), such as GPT-4, to understand and analyze code changes in pull requests (PRs). Internally, CodeRabbit’s AI architecture is designed for context-aware analysis, integrating with static analysis tools like Semgrep for security checks and ESLint for style enforcement. The tool learns from team practices over time, adapting its recommendations to align with specific coding standards and preferences.

Challenges: A significant challenge is the potential for AI to misinterpret non-trivial business logic due to its lack of domain-specific knowledge. For instance, while CodeRabbit can detect syntax errors or common vulnerabilities, it may struggle with complex business rules or edge cases that require human understanding. Additionally, integrating such tools into existing workflows may require initial setup and adjustment, though CodeRabbit claims instant setup with no complex configuration.

Impact: By automating code reviews, tools like CodeRabbit reduce manual review time by up to 50%, allowing developers to focus on higher-level tasks. This not only saves time but also reduces cognitive load, as developers no longer need to manually scan through large PRs. A GitLab survey highlighted that manual code reviews are a top cause of developer burnout due to delays and inconsistent feedback. AI tools mitigate this by providing consistent, actionable feedback, improving productivity and reducing mental strain.

Case Study: At KeyValue Software Systems, implementing CodeRabbit reduced code review time by 90% for their Golang and Python projects, allowing developers to focus on feature development rather than repetitive review tasks.

2. Automated Unit Test Generation

Unit testing ensures that individual code components function correctly, but writing these tests manually can be time-consuming. AI tools automate this process by generating comprehensive test suites. Diffblue Cover, for example, uses reinforcement learning to create unit tests for Java code. Internally, Diffblue’s reinforcement learning agents interact with the code, learning to write tests that maximize coverage and reflect every behavior of methods. These agents are trained to understand method functionality and generate tests autonomously, even for complex scenarios.

Challenges: Handling large, complex codebases with numerous dependencies remains a challenge. Additionally, ensuring that generated tests are meaningful and not just covering trivial cases requires sophisticated algorithms. For instance, Diffblue Cover must balance test coverage with test relevance to avoid generating unnecessary or redundant tests.

Impact: Automated test generation saves developers significant time – Diffblue Cover claims to generate tests 250x faster than manual methods, increasing code coverage by 20%. This allows developers to focus on writing new code or fixing bugs rather than repetitive testing tasks. By reducing the need for manual test writing, these tools lower cognitive load, as developers can rely on AI to handle the tedious aspects of testing. A Diffblue case study showed a 90% reduction in test writing time, enabling teams to focus on higher-value tasks.

Case Study: A financial services firm using Diffblue Cover reported a 30% increase in test coverage and a 50% reduction in regression bugs within six months, significantly reducing the mental burden on developers during code changes.

3. Behavioral Testing and Automated UI Testing

Behavioral testing ensures software behaves as expected, while UI testing verifies functionality and appearance across devices and browsers. AI automates these processes, enhancing scalability and efficiency. Applitools, for instance, uses Visual AI to detect visual regressions by comparing screenshots of the UI with predefined baselines. Internally, Applitools captures screenshots and uses AI to analyze visual differences, identifying issues like layout shifts or color inconsistencies. It can handle dynamic content and supports cross-browser and cross-device testing.

Challenges: One challenge is handling dynamic UI elements that change based on user interactions or data. Ensuring that the AI correctly identifies meaningful visual differences while ignoring irrelevant ones, such as anti-aliasing or minor layout shifts, is crucial. Additionally, maintaining accurate baselines as the UI evolves can be resource-intensive.

Impact: Automated UI testing reduces manual testing effort by up to 50%, allowing QA teams to test more scenarios in less time. This leads to faster release cycles and reduces cognitive load on developers, as they can rely on automated tests to catch visual regressions.

Case Study: An e-commerce platform using Applitools reported a noticeable reduction in UI-related bugs post-release, as developers could confidently make UI changes without fear of introducing visual regressions.

4. Continuous Integration and Continuous Deployment (CI/CD) Automation

CI/CD pipelines automate the build, test, and deployment processes. AI enhances these pipelines by predicting failures and optimizing workflows. Harness, for example, uses AI to predict deployment failures based on historical data. Internally, Harness collects logs, metrics, and outcomes from previous deployments to train machine learning models that analyze patterns and predict potential issues. These models can identify risky deployments before they reach production.

Challenges: Ensuring access to high-quality labeled data is essential, as deployments can be complex with multiple failure modes. Additionally, models must be updated regularly to account for changes in the codebase and environments. False positives or missed critical issues can undermine trust in the system.

Impact: By predicting deployment failures, Harness reduces deployment failures by up to 70%, saving time and resources. This reduces cognitive load on DevOps teams, as they no longer need to constantly monitor deployments and react to failures. Automated CI/CD pipelines also enable faster feedback loops, allowing developers to iterate more rapidly.

Case Study: A tech startup using Harness reported a 50% reduction in deployment-related incidents and a 30% increase in deployment frequency, as AI-driven predictions prevented problematic releases.

5. Intelligent Bug Tracking and Prioritization

Bug tracking is critical, but manual prioritization can be inefficient. AI automates detection and prioritization, enhancing resolution speed. Bugasura, for instance, uses AI to classify and prioritize bugs based on severity and impact. Internally, Bugasura likely employs machine learning models trained on historical bug data to classify new bugs and assign priorities. It may also use natural language processing to extract relevant information from bug reports.

Challenges: Accurately classifying bugs, especially in complex systems with multiple causes or symptoms, is a significant challenge. Avoiding false positives and ensuring critical issues are not overlooked is crucial. Additionally, integrating with existing project management tools can introduce compatibility issues.

Impact: Intelligent bug tracking reduces the time spent on manual triage by up to 40%, allowing developers to focus on fixing the most critical issues first. This leads to faster resolution times and improved software quality. By automating prioritization, these tools reduce cognitive load, as developers no longer need to manually sort through bug reports.

Case Study: A SaaS company using Bugasura reduced their bug resolution time by 30% and improved customer satisfaction scores by 15%, as critical bugs were addressed more quickly.

6. Dependency Management and Vulnerability Detection

Managing dependencies and detecting vulnerabilities early is crucial for security. AI tools scan for risks and outdated dependencies without deploying agents. Wiz, for example, uses AI to analyze cloud environments for vulnerabilities. Internally, Wiz collects data from various cloud services (e.g., AWS, Azure, GCP) and uses machine learning models to identify misconfigurations, outdated software, and other security weaknesses. It analyzes relationships between components to uncover potential attack paths.

Challenges: Keeping up with the rapidly evolving cloud environments and constant updates to cloud services is a major challenge. Minimizing false positives while ensuring all critical vulnerabilities are detected is also important. Additionally, ensuring compliance with security standards across diverse environments can be complex.

Impact: Automated vulnerability detection reduces manual scanning efforts, allowing security teams to focus on remediation. By providing prioritized lists of vulnerabilities, these tools help manage workload effectively, reducing cognitive load. Wiz claims to reduce vulnerability identification time by 30%, enhancing overall security posture.

Case Study: A fintech firm using Wiz identified and patched 50% more critical vulnerabilities in their cloud environment compared to traditional methods, reducing their risk exposure significantly.

Conclusion

Left-shifting quality, enhanced by AI, is a critical strategy for modern software development, reducing costs, improving quality, and accelerating delivery. AI-powered tools automate and optimize QA processes, from code review to vulnerability detection, enabling teams to catch issues early and deliver brilliance. As AI continues to evolve, with trends like generative AI for test generation and predictive analytics, the future promises even greater efficiency. Organizations adopting these techniques can transform their development processes, achieving both speed and excellence.

Optimizing Android for Scale: Storage Strategies for Modern Mobile Ecosystems

Posted on by Editorial Team 

Contributed by Parth Menon, Software Engineer

Many of us in today’s age are familiar with the term Android. The latter has been among the most adopted mobile technologies in the world, powering billions of devices across the globe. As it scales, the need for mobile storage management efficiently has never been more important. Applications are becoming increasingly complex and store large media files, intricate data sets, and an increasing number of assets. Consequently, the performance and user experience of these apps have become vital challenges to address. What’s more, modern applications are no longer built by a single team. In fact, some of the world’s largest apps, like Facebook, Instagram, Deliveroo, and Google, are developed by multiple teams and organizations spread across different countries, time zones, and continents. This vast, global collaboration adds further layers of complexity to both app development and storage management. This article will delve into storage strategies that support scalability, enhance user experience, and optimize app performance while navigating the challenges of such widespread teamwork. 

The Increasingly Important World of Efficient Storage in Mobile Ecosystems

Starting with mobile storage, it is the backbone of performance in Android devices, from app load times to user interactions with content. Unlike desktops or laptops,where storage is scalable and users can easily upgrade their storage capacity, mobile devices are limited by the storage they come with. This means that once you buy a mobile device, you’re stuck with its storage capacity, making it even more important to optimize how an app manages its data. Additionally,  users interact with mobile devices at a faster pace, frequently switching between apps, which demands that apps load quickly and respond instantly. Basically a well-optimized storage system ensures that apps run efficiently while still offering rich user experiences.

Why It Matters:

User Expectations: First reason is that users on mobile expect the app to be quick and responsive. When applications consume a lot of storage or take longer to load due to poor data management, this results in user frustration. As a matter of fact, a recent report from UXCam indicates that 90% of users have stopped using an app due to poor performance, and 88% will abandon an app if it consistently experiences glitches or technical bugs. Additionally, 21% of mobile apps have been used only once, underscoring the necessity for apps to deliver immediate value and seamless functionality to engage users effectively.

Developer Challenges: Secondly, Android developers are tasked with the job of creating applications that scale well across the board, considering a wide field of devices that come with limited amounts of internal storage. Variations in hardware, screen size, and amount of storage have placed increasing demands on developers to find flexible and efficient means of storing data on Android, ensuring optimal performance regardless of the device type.

Key Strategies for Optimizing Android Storage

1. Using Scoped Storage for Security and Efficiency

Moving to scoped storage, it was an important behavior change that was introduced with time in Android 10, that fundamentally altered how apps share files and access external data. Apps used to have nearly free run of the device, for better or worse, due to the previous paradigm. In contrast, scoped storage provides a restricted environment whereby an app is only allowed to access specific directories. 

In addition, developers should migrate their applications to scoped storage to align with the privacy standards set by Google. This scoped storage not only restricts data access but also increases user control over which data can be shared, hence improving trust and security.

For instance, the MediaStore API can be used to manage user media files, such as photos and videos, without having direct access to other sensitive files. This API is quite handy in interacting with media files while abiding by scoped storage guidelines.

Real-World Example:

Applications such as Spotify and WhatsApp serve as examples for the successful usage of scoped storage to adapt with extended standards of privacy protection under the Android environment. It isolates apps from any interaction with external files or system data other than the ones they actually have created. For example, WhatsApp by default keeps all of its files in its scoped storage but does allow users to store media outside of it on the device, depending on their choice. This balances security and user control, enabling these apps to scale to millions of users while keeping both performance and privacy.

2. Effective Strategy for Caching Data

In order to optimize app performance and user experience in data-heavy applications, effective caching strategies play a vital role. Caching is a critical method for enhancing mobile app performance, especially in data-heavy apps. Cache storage temporarily holds frequently accessed data, reducing the need to repeatedly fetch it from remote servers or databases, thus improving speed and responsiveness. However, without proper management, caches can grow uncontrollably, leading to unnecessary storage consumption and slower app performance.

Best Practices for Caching:

Caching is best implemented by apps themselves, so by thoughtfully managing caching, apps can enhance performance and optimize user experience while conserving device resources.

A good example would be Youtube, which is an adaptive caching through its Smart Downloads feature. This functionality downloads and caches recommended videos, ensuring they are available for users even without internet connectivity. Additionally, YouTube’s approach optimizes cache size based on available storage, preventing bloat and performance regressions while maintaining a seamless user experience.

3. Using Cloud Integration to Expand Storage

Cloud storage solutions have revolutionized how apps manage data, giving a practical way in which the limitations brought about by local device storage can be overcome. By using the cloud, applications can offset large files and backups, thus helping the application run on devices with constrained storage smoothly. However, it’s important to note that cloud integration often benefits apps when there is a backend server for doing the processing.

For instance, there is Google Photos for seamless cloud integration. The app itself not only saves the local device from storage pressure by backing up the photos and videos on the cloud but also provides an opportunity for the backend servers to process the content by automatically adding tags, geolocation metadata, and other contextual information that enhance the power of search and retrieval. This processing, which would be inefficient or impossible on a local device, greatly improves the user experience by offering faster and more accurate search results.

Best Practices for Cloud Integration:

  • Selective Syncing: Allow users to decide which data gets uploaded to the cloud and which remains local, giving them greater control over their storage.
  • On-Demand Downloads: Only fetch data from the cloud when necessary to minimize storage usage on the device.
  • Real-Time Updates: Implement real-time synchronization with cloud storage to ensure that data remains up-to-date without manual intervention.
  • Enhanced User Privacy: Use encryption and secure transfer protocols to protect user data both in transit and at rest.

So by utilizing cloud storage effectively, developers can optimize app performance, conserve local device resources, and unlock advanced functionalities through server side processing. This strategy is particularly valuable for apps managing large media files or requiring computationally intensive features that extend beyond the capabilities of a mobile device.

Advanced Solutions: Beyond Traditional Storage Management

Custom Scoped Storage Management 

While the above solutions use already existing methods to improve Storage Management on device, as the application scales, it becomes harder to manage storage at an app level with multiple sub products and services competing for the same storage space.

As applications are sandboxed since Android 9, developers have 2 main directories to store files.

Context.getFilesDir() returns a directory within the app’s sandbox where devs can store files related to the app. These files are generally only deleted when the app is uninstalled or all data of the app is cleared.
Context.getCacheDir() returns a similar directory but where cached files are stored. Cached files should be cleaned up by the app, but they can also be cleaned up by the OS or other third party storage cleaner apps.

As the app scales, a way to provide better storage management would be to provide a single entry point or service that acts as a Storage Layer above Android’s APIs.
The Storage Layer can then provide managed subdirectories to products and services, under the Cache or Files app sandbox directories based on configuration.

This API layer has many advantages:

  1. Ownership: The subdirectory requested by the product or service has clear ownership of it and all files under it. No other product or service should access or make changes within this directory
  2. Automatic cleanup: A great advantage of having a managed directory is that it can be automatically cleaned up after use. The configuration can have a parameter which states how long the data should be kept, which prevents stale data from taking up precious space on device
  3. Limits: Having managed partitioned directories means that it is possible to set limits to the data contained within it. Once the limit is exceeded, the directory can be cleaned up. Additionally, other cleanup algorithms can also be used to retain and re-use individual files in the directory which are frequently used, such as LRU based cleanup
  4. Versioning: App scaling and growing over time can mean changes to the data being stored, additional metadata or entire change to the storage itself. These can be versioned from the Storage Layer with migrators in place to move data between versions.
  5. User Scoping: An additional boon to having managed storage is User Scoped storage.
    Products and Services that have user data can be stored to UserScoped subdirectories, which can be auto cleaned up when the user logs out or switches. This significantly boosts the privacy of the app by ensuring no user data is kept once the user removes their account.

Conclusion: Towards Smart Storage Ecosystem

In conclusion, the Android mobile device storage landscape is evolving at a very fast pace. Optimizing storage in today’s world is no longer about just managing space; rather, it has to do with creating intelligent, scalable systems that balance user expectations with app performance. The more complex mobile apps are getting the greater the demand for strong storage solutions which can scale across millions of devices.

Further, developers are armed with a host of other features, from scoped storage to custom storage management  optimizations and embracing cloud-based solutions. These innovations ensure that the developers create applications that scale efficiently and offer seamless experiences that keep users coming back for more.

However, the big question into the future is, with further development in AI and cloud computing, how will these continue to redefine mobile app experiences and change the way we use our devices? The answer will likely depend on continued innovation and collaboration across the entire Android ecosystem.

Data Fabric and Data Mesh: Complementary Forces or Competing Paradigms?

Posted on by Editorial Team

As the world continues to change, two frameworks have emerged to help businesses each manage their data ecosystems – Data Fabric and Data Mesh. While both these frameworks aim to simplify a business’s data governance, integration, and access, they differ quite a lot in their philosophy and how they operate. Data Fabric focuses more on technological orchestration over a distributed environment. Alternatively, Data Mesh focuses more on structural decentralization and domain-centric autonomy. This article looks at the powerful cloud-based architecture that integrates these two frameworks through its definitions, strengths, limitations, and the potential for synergy.

(um) let’s make tech work for us)

What is Data Fabric?

The Data Fabric concept originated in 2015 and came into focus after Gartner included it in the top analysis trends of 2020. In the DAMA DMBOK2 glossary, data architecture is defined as the plan for how to manage an organization’s data assets in a way that model of the organization’s data structures. Data Fabric implements this by offering a unified framework that automatically and logically integrates multiple disjointed data systems into one entity. 

Simply put, Data Fabric is a singular architectural layer that sits on top of multiple heterogeneous data ecosystems – on-premises systems, cloud infrastructures, edge servers –  and abstracts their individual complexities. It uses and combines several data integration approaches like the use of special data access interfaces (APIs), reusable data pipelines, automation through metadata, and AI orchestration to provide and facilitate non-restricted access and processing. Unlike older methods of data virtualization, which assisted in constructing a logical view, Data Fabric combines with the essence of containerization, which allows better management, control, and governance making masking it more powerful for modernizing applications than traditional methods.

Key Features of Data Fabric

  • Centralized Integration Layer: A virtualized access layer unifies data silos, governed by a central authority enforcing enterprise standards.
  • Hybrid Multi-Cloud Support: Consistent data management across diverse environments, ensuring visibility, security, and analytics readiness.
  • Low-Code/No-Code Enablement: Platforms like the Arenadata Enterprise Data Platform or Cloudera Data Platform simplify implementation with user-friendly tools and prebuilt services.

Practical Example: Fraud Detection with Data Fabric

Consider a financial institution building a fraud detection system:

  1. An ETL pipeline extracts customer claims data from multiple sources (e.g., CRM, transaction logs).
  2. Data is centralized in a governed repository (e.g., a data lake on Hadoop or AWS S3).
  3. An API layer, enriched with business rules (e.g., anomaly detection logic), connects tables and exposes the unified dataset to downstream applications.


While this approach excels at technical integration, it often sidesteps critical organizational aspects – such as data ownership, trust, and governance processes—leading to potential bottlenecks in scalability and adoption.

How Data Mesh Works

Data Mesh, introduced around 2019, is a new framework of data architecture that puts a greater emphasis on people rather than technology and processes. Like DDD, Data Mesh advocates for Domain-oriented decentralization, which promotes the fragmentation of data ownership among business units. Unlike Data Fabric, which controls everything from a single point, Data Mesh assigns domain teams with the responsibility of treating data as a product that can be owned, accessed, and interacted with in a self-service manner. 

Core Principles of Data Mesh

  • Domain-Oriented Decentralization: The closest teams to the data, whether it be its consumption or generation, have the ownership and management of the data. 
  • Data as a Product: More than just a simple dataset, each dataset can be marketed and comes with features such as access controls and metadata. 
  • Self-Service Infrastructure: Centralized domain teams are able to function autonomously because of a centralized platform. 
  • Federated Governance: Domains without a central data governance point are controlled centrally in terms of standards, data policies, and interfacing.

Practical Example: Fraud Detection with Data Mesh

Using the same fraud detection scenario:

  1. A domain team (e.g., the claims processing unit) defines and owns an ETL/ELT job to ingest claims data.
  2.  Datasets (e.g., claims, transactions, customer profiles) are stored separately, each with a designated owner.
  3.  A data product owner aggregates these datasets, writing logic to join them into a cohesive fraud detection model, delivered via an API or event stream.

This approach fosters accountability and trust by embedding governance into the process from the outset. However, its reliance on decentralized teams can strain organizations lacking mature data cultures or robust tooling.

Emerging Tools

Data Mesh is still maturing technologically. Google’s BigLake, launched in 2022, exemplifies an early attempt to support Data Mesh principles by enabling domain-specific data lakes with unified governance across structured and unstructured data.

Data Fabric works best with complex siloed infrastructures since it offers a top-down approach to data access. On the other hand, Data Mesh performs well in decentralized organizations that are willing to undergo a cultural shift and give more emphasis on trust and agility as compared to technical standardization.

Just like data fabric and data mesh, enterprise operational context and digital transformation journey determines the scope of its existence. The cloud provides a platform where both approaches can be integrated. Consider an architecture where there exists an event bus (for example Apache Kafka), which streams data to many different consumers. The consumers could include AWS S3, which acts as a data lake, and ETL pipelines (AirFlow for batch and NiFi for streaming), which serve to integrate operational and historical data. Add a robust Master Data Management (MDM) layer and analytics will be of good quality. 

This is the integration point where synergy shines: the centralized integration of data fabric sets up the infrastructure and data mesh domain autonomy makes it possible to innovate. A cloud native application platform which enables and controls innovation is the result. Business Intelligence (BI) dashboard is an example, which could be drawing the Mesh IoT dashboard clean data products, while Fabric governs seamless access to data.

A Call to Innovate

Marrying these paradigms isn’t without hurdles. Architects and engineers must grapple with:

  • Migration Complexity: How do you transition on-premises data to the cloud without disruption?
  •  Real-Time vs. Batch: Can the platform balance speed and depth to meet business demands?
  •  Data Quality: How do you embed quality checks into a decentralized model?
  •  Security and Access: What federated security model ensures ease without compromising safety?
  •  Lifecycle Management: How do you govern data from creation to destruction in a hybrid setup?


Moreover, the cloud isn’t a silver bullet. Relational databases often fall short for advanced analytics compared to NoSQL, and data lake security models can hinder experimentation. Siloed data and duplication further complicate scalability, while shifting from centralized to decentralized governance requires a cultural leap.

The Verdict: Together, Not Versus

So, is it Data Fabric versus Data Mesh? These methods have no real conflict; they work hand in hand. Data Fabric shows the threads of a technology metaphor for a superordinate access to information, while Data Mesh gives authority to the operational teams to manage their data. In a cloud-powered ecosystem, they have the potential to revolutionize data management by merging centralization’s productivity with decentralization’s creativity. The challenge that arises is not what to select, but how to combine the multifarious assets into a harmonious orchestra that nurtures trust, economic agility, and value to the enterprise. As the instruments undergo changes and institutions transform, these two concepts may as well be the paradigm shift that data architecture has long been waiting for, shaken, stirred and beautifully blended.