How Gen AI Can Transform Software Engineering

Generative AI has enormous potential for business use cases, and its application to software engineering is equally promising.

In our experience, development activities, including automated test and deployment scripts, account for only 30-50% of the time and effort spent across the software engineering lifecycle. Within that, only a fraction of the time and effort is spent in actual coding. Hence, to realize the true promise of Generative AI in software engineering, we need to look across the entire lifecycle.

A typical software engineering lifecycle involves a number of different personas (Product Owner, Business Analyst, Architect, Quality Assurance/ Tech Leads, Developer, Quality/ DevSecOps/ Platform Engineers), each using their own tools and producing a distinct set of artifacts. Integrating these different tools through a combination of Gen AI software engineering extensions and services will help streamline the flow of artifacts through the lifecycle, formalize the hand-off reviews, enable automated derivation of initial versions of related artifacts, etc.

As an art-of-the-possible exercise, we developed extensions (for VS Code IDE and Chrome Browser at this time) incorporating the above considerations. Our early experimentation suggests that Generative AI has the potential to enable more complete and consistent artifacts. This results in higher quality, productivity and agility, reducing churn and cycle time, across parts of the software engineering lifecycle that AI coding assistants do not currently address.

Complementary approaches to automate repetitive activities through smart templating, leveraging Generative AI and traditional artifact generation and completion techniques can help save time, let the team focus on higher-value activities and improve overall satisfaction. However, there are key considerations in order to do this at scale across many teams and team members. To enable teams to become high-performant, the Gen AI software engineering extensions and services need to provide capabilities around standardization and templatization of standard solution patterns (archetypes) and formalize the definition and automation of steps of doneness for each artifact type.

Read our perspective paper for more insights on How Gen AI Can Transform Software Engineering through streamlined processes, automated tasks, and augmented collaboration, bringing faster, higher-quality software delivery.