Today, with the growing obsession with applying Artificial Intelligence (AI), particularly Machine Learning (ML), to software across various contexts, much of the focus has been on the effectiveness of AI models, often measured through common metrics such as F1- score, while fairness receives relatively little attention. This paper presents a review of existing gray literature, examining fairness requirements in AI context, with a focus on how they are defined across various application domains, managed throughout the Software Development Life Cycle (SDLC), and the causes, as well as the corresponding consequences of their violation by AI models. Our gray literature investigation shows various definitions of fairness requirements in AI systems, commonly emphasizing non-discrimination and equal treatment across different demographic and social attributes. Fairness requirement management practices vary across the SDLC, particularly in model training and bias mitigation, fairness monitoring and evaluation, and data handling practices. Fairness requirement violations are frequently linked, but not limited, to data representation bias, algorithmic and model design bias, human judgment, and evaluation and transparency gaps. The corresponding consequences include harm in a broad sense, encompassing specific professional and societal impacts as key examples, stereotype reinforcement, data and privacy risks, and loss of trust and legitimacy in AI-supported decisions. These findings emphasize the need for consistent frameworks and practices to integrate fairness into AI software, paying as much attention to fairness as to effectiveness.

Generative AI (GenAI) has recently emerged as a groundbreaking force in Software Engineering, capable of generating code, identifying bugs, recommending fixes, and supporting quality assurance. While its use in coding tasks shows considerable promise, applying GenAI across the entire Software Development Life Cycle (SDLC) has not yet been fully explored. Critical uncertainties in areas such as reliability, accountability, security, and data privacy demand deeper investigation and coordinated action. The GENIUS project, comprising over 30 European industrial and academic partners, aims to address these challenges by advancing AI integration across all SDLC phases. It focuses on GenAI's potential, the development of innovative tools, and emerging research challenges, actively shaping the future of software engineering. This vision paper presents a shared perspective on the future of GenAI-driven software engineering, grounded in cross-sector dialogue as well as experiences and findings within the GENIUS consortium. The paper explores four central elements: (1) a structured overview of current challenges in GenAI adoption across the SDLC; (2) a forward-looking vision outlining key technological and methodological advances expected over the next five years; (3) anticipated shifts in the roles and required skill sets of software professionals; and (4) the contribution of GENIUS in realising this transformation through practical tools and industrial validation. This paper focuses on aligning technical innovation with business relevance. It aims to inform both research agendas and industrial strategies, providing a foundation for reliable, scalable, and industry-ready GenAI solutions for software engineering teams.

Traditional banks face significant challenges in digital transformation, primarily due to legacy system constraints and fragmented ownership. Recent incidents show that such fragmentation often results in superficial incident resolutions, leaving root causes unaddressed and causing recurring failures. We introduce a novel approach to post-incident analysis, integrating knowledge-based GenAI agents with the "Five Whys" technique to examine problem descriptions and change request data. This method uncovered that approximately 70% of the incidents previously attributed to management or vendor failures were due to underlying internal code issues. We present a case study to show the impact of our method. By scanning over 5,000 projects, we identified over 400 files with a similar root cause. Overall, we leverage the knowledge-based agents to automate and elevate root cause analysis, transforming it into a more proactive process. These agents can be applied across other phases of the software development lifecycle, further improving development processes.

Where exactly are we on this transformative journey? How are enterprises navigating this new terrain--and what's still ahead? To investigate how generative AI is impacting the SDLC, MIT Technology Review Insights surveyed more than 300 business leaders about how they're using the technology in their software and product lifecycles. The findings reveal that generative AI has rich potential to revolutionize software development, but that many enterprises are still in the early stages of realizing its full impact. While adoption is widespread and accelerating, there are significant untapped opportunities.

Planning and control for uncertain contact systems is challenging as it is not clear how to propagate uncertainty for planning. Contact-rich tasks can be modeled efficiently using complementarity constraints among other techniques. In this paper, we present a stochastic optimization technique with chance constraints for systems with stochastic complementarity constraints. We use a particle filter-based approach to propagate moments for stochastic complementarity system. To circumvent the issues of open-loop chance constrained planning, we propose a contact-aware controller for covariance steering of the complementarity system. Our optimization problem is formulated as Non-Linear Programming (NLP) using bilevel optimization. We present an important-particle algorithm for numerical efficiency for the underlying control problem. We verify that our contact-aware closed-loop controller is able to steer the covariance of the states under stochastic contact-rich tasks.

By combining the roles of software development and IT operations, DevOps (opens in new tab) often encompasses so many tools and skills that too many of us get stuck working in a complex and time-consuming environment. Time that could be spent solving problems gets wasted on mundane tasks. By using artificial intelligence (AI), DevOps can automate complicated tasks that are easy for computers but hard (or boring) for humans. AI can also help streamline processes across the software development lifecycle (SDLC), allowing DevOps to focus on the work itself. In this guide, we cover how AI can be used throughout the DevOps cycle to improve productivity and security.

We understand your need to achieve impeccable quality at maximum speed. Our bespoke test automation strategies and intelligent automation frameworks minimize risks while focusing on the right interfaces for testing. Test Automation can solve the challenges of rapid development cycles and your need to respond to the escalating customer demands while preserving quality. Our Automated testing approaches can empower your business by ensuring precision through rapid, seamless, automated test suites. Minimize your overall cost of quality as we mitigate risks early in the SDLC.

AI (Artificial Intelligence) as we know it, is the reason behind all the advancements that we see in today's world, on the technology front (of course!). Soon, we will see machines or robots taking over most of the humane work. From healthcare to insurance, banking to finance, eCommerce to Edtech and Fintech, we can see the footprints and lasting impressions of AI in every industry domain and Software/IT is no exception. While we talk about software engineering, software development and all related aspects of SDLC (Software Development Lifecycle) come under it. From analyzing the requirements to designing, developing, deploying, and testing, software engineering vastly covers all these areas and more.

Artificial Intelligence-based testing can offer several benefits to testers in the form of enabling automation in writing test cases, improving test coverage, making predictive analysis, and identifying bugs early, and saving significant costs thereon. Quality testing has become a critical factor in ensuring that a software application delivers customer satisfaction. It involves assessing and analyzing the software application under certain conditions to know its threshold and attendant risks when implemented. However, with software build life cycles turning more complex and delivery turnarounds reducing, QA testers need to identify any inherent glitches in the application quickly and effectively. Thereupon, the information is passed on to the development team to fix the glitches and turn the application worthy of being delivered or implemented. The complexity of any software application under development has become more challenging due to the focus on quick releases. So, what used to take a month for delivery has become a weekly affair with updates factored in almost on a daily basis. This has made the job of software testers all the more challenging and has created conditions to make testing smarter, efficient, accurate, and predictive. No wonder AI-based testing has become the go-to area for testers to implement a continuous automated and precision-based testing process. In fact, AI testing has begun to play a critical role in quality control for a host of business and industry verticals. It helps to set in motion a slew of measures that is predictive, accurate, and objective. By freeing up human effort, AI-led quality assurance speeds up the SDLC to achieve outcomes as mandated by Agile and DevOps methodologies. Further, given that human testers have subjective biases, especially in a manual testing setup where the quality of testing can change according to the quality of the testers, Artificial Intelligence app testing delivers on parameters like speed, cost, accuracy, performance, safety, and scalability, seamlessly and effectively. AI can not only find glitches in the software application but perform root cause analysis to understand the source of such glitches. Benefits of using AI in QA testing Since orchestrating test automation across CI/CD pipelines has become challenging, AI testing services are used to enable smart testing in applications that have multiple dependencies, resources, and technologies. The benefits of using AI-based testing are as follows: Automation of writing test cases: One of the challenges QA testers face while conducting test automation is writing test cases. They often end up writing large test cases to detect a minor bug thereby impacting test efficiency. AI test automation can ensure the writing of precise test cases quickly and accurately. Besides, when developers/testers write test cases, instead of choosing the most efficient option – the one that generates the least redundant data, they write test cases that they are comfortable with. AI, on the other hand, can choose the most efficient test option where there would not be bottlenecks, manual involvement, or redundant data. Improve overall test coverage: AI-driven testing can expand the scope of testing by looking into the memory and file contents, data tables, or internal program states. This way it can determine if the software application is behaving as expected. AI can execute several test cases in every test run, which is not possible with manual testing. Identify bugs early and deliver cost savings: Bugs or glitches can be very expensive to fix if identified later in the SDLC. However, AI test automation can offer instant feedback to the developers about the presence of bugs and deliver significant cost savings. It is important to remember that the cost of fixing bugs after product release can be four to five times expensive than the ones identified in the SDLC. Predictive analysis: Artificial Intelligence can analyze the existing customer data to predict how customers’ browsing habits and needs would evolve. This helps developers and testers to be a step ahead of users’ choices and offer quality products or services in alignment with their expectations. With AI-ML testing, the test platforms get better at analyzing user behavior and making precise predictions. Conclusion In a day and age where going digital is the ultimate goal for enterprises to reach out to their customers and be competitive, AI-powered platforms have become a tangible reality. AI-based testing helps to optimize test automation and let the tests to self-heal and execute. It helps in automating more areas of testing such as UI testing and visual validation. By analyzing large volumes of data, Artificial Intelligence can create comprehensive and precise test cases to validate the smallest bugs in the system. The post What role does AI play in QA appeared first on NASSCOM Community |The Official Community of Indian IT Industry.

To learn about the current and future state of machine learning (ML) in software development, we gathered insights from IT professionals from 16 solution providers. We asked, "What do developers need to keep in mind when using machine learning in the SDLC?" Here's what we learned: The biggest issue for ML is viewing it as an omnipotent savior of the SDLC, thereby negating the need to adhere to traditional SDLC design and protocol. ML can greatly improve efficiency and allow developers to better allocate their time to actions that require human input. It cannot, however, completely take the place of conscientious, diligent and thoughtful software planning, design, development, and version control. Use the tools provided by public cloud providers. They are self-paced, painless, and you can get certified.