AI First Software Engineering – Intersection of AI & Software Engineering

Software engineering and Artificial Intelligence (AI), two critical areas that have each experienced significant improvements, are now interleaving in a way that will radically alter how software is developed in the future. It is crucial to register this convergence and its revolutionary possibilities as we move into an "AI-First" era.

Since its conception, AI has seen substantial evolution. AI has progressed over time, from the era of rule-based systems to the rise of machine learning and deep learning. Today, we are seeing the rise of Generative AI models like GPT-4, which can recognize patterns in data and create new content that mimics the learned structure. It can not only do natural language generation but also extend its capabilities to create original music, art, design models, and even generate software code. This has the potential to transform the way we develop software by automating many of the operations and altering the ways of working irreversibly thereby holding forth the promise of a future with previously unimaginable possibilities.

Software engineering has simultaneously seen major developments and moved from monolithic architectures to microservices, micro front ends, Waterfall to Agile, DevSecOps & SRE (Site Reliability Engineering), from on prem to cloud, newer programming paradigms, advent of open-source software and more. Most recent developments in software engineering are stressing holistic, effective, and collaborative approaches coupled with the evolution of the technology stacks and infrastructure.

Generative AI and sophisticated software engineering together have tremendous potential. Several activities in the software development lifecycle can be accelerated by generative AI. The promise of greater productivity is significant and includes code generation, test automation, bug fixes, and more. Coupled with Low Code approaches, a Reuse strategy and a higher degree of Engineering and Automation maturity can create the right environment for unprecedented improvements in both Quality & Productivity.

Beyond increasing output and enhancing quality, intuitive and tailored user experiences can be created by AI's ability to examine and comprehend complicated patterns. Resource optimization powered by AI can lead to better planning and resource allocation. There are clearly many more facets of the value chain that will get impacted by the infusion of AI and will cause changes in the working methods. This transformation involves skill enhancement, the creation of new positions, and a shift in development focus from coding to strategic planning, design thinking etc. Cross-functional teamwork and a better grasp of data management will be essential. Furthermore, ensuring ethical and responsible AI development will become a key component of the process, assuring fairness, transparency, and accountability in AI systems.

The path to an AI-First era is not without its challenges and inherent difficulties. Data privacy, security, and AI bias are all issues that need careful consideration and must be addressed diligently. Overreliance on AI may also result in reduced human control, putting systems exposed to AI-specific hazards. Safeguards and measures to drive responsible AI across the enterprise over a range of use cases is critical and must be before we scale its usage. As this happens, Generative AI will become increasingly integrated and common in software engineering, and we may expect more intelligent, efficient, and secure software development as these models learn from increasingly diverse and larger datasets.

Our own experience over the past couple of years has been quite revealing, working with several of our partners as well as teams within the organization we found nuggets that work quite well in our ecosystem. We have evaluated over 50 different tools and platforms across the value chain that infuses AI into various tasks and activities in the Life Cycle. We have also followed closely the development of narrow transformers within the organization that holds a lot of promise for the future with our ability to train open-source models with our own data and use them in the context specific to a portfolio. Our early pilots with GitHub Copilot supplied us tremendous insights into the working of the codex model and the implications it would have on application development and management in the future. We found teams and individuals accelerating their development efforts significantly on the back of substantial amounts of their code generated with smart prompts. Not just this, we could also see a substantial improvement in the quality of the code as well as higher levels of security compliance. Across the value chain there are several areas that are now being touched in ways that are going to be irreversible in the amount of effectiveness that AI can bring in those – prominent amongst these are user story creation, estimation, design & prototyping, development, testing, deployment, operations, and decision making across the board with smart analytics.

We are on the verge of an AI-first age in software engineering, where the combination of AI and human skill has the potential to transform software development. It is a voyage of constant learning, adaptation, and growth, filled with both immense opportunities and some daunting challenges. As we embrace artificial intelligence, the future of software engineering will be anything but routine and is poised to unleash a new wave of productivity combined with better quality.