Purpose of the Workshop

As the adoption of generative AI-based tools gains momentum, software engineers are facing the reality of envisioning a future where software-reliant systems will be designed, tested, deployed, and maintained with AI-enabled tools playing a greater role than they do today. The analysis of developer data to date reveals that while generative AI tools offer numerous advantages, they are also non-trivial to apply at scale.

In response, the research community has begun evaluating the implications of generative AI for software engineering. Examples range from researching LLMs (large language models) for code or investigating use of foundation models for various software development activities to investigating the democratization of software with prompt-based interactions may replace portions of software capabilities.

While significant ongoing work is attempting to understand how generative AI can improve various software activities, a concerted emphasis on how generative AI impacts the overall orchestration of SDLC workflows is missing. To fill this gap, our proposed workshop aims to galvanize the research community’s focus on the implications of the changing software engineering landscape by understanding how the SDLC is being transformed by automating and enhancing various development stages via generative AI approaches. In particular, the workshop will focus on how end-to-end SDLC workflows may be influenced and what data should be collected to better assess end-to-end outcomes. 

Special Issue in Automated Software Engineering Journal 

Authors of select papers will be invited to submit extended versions of their work to Springer’s Automated Software Engineering journal. Details to follow.