Some things never change: how far generative AI can really change software engineering practice
Aline de Campos, Jorge Melegati, Nicolas Nascimento, Rafael Chanin, Afonso Sales, Igor Wiese
TL;DR
This study investigates which software engineering activities GenAI is unlikely to deeply transform in the near to mid term by surveying SE practitioners and contrasting results with established roadmaps. Using a mixed-methods online survey of professionals from large IT companies, the authors identify productivity and coding-related gains as plausible, while human expertise, creativity, and project management are seen as resilient to automation. The results highlight practical benefits (e.g., improved testing, documentation, and code quality) alongside challenges in ethics, data security, and organizational strategy. The findings offer a nuanced view of GenAI's role in SE, suggesting a collaborative future where GenAI augments but does not replace human-centric processes, and propose directions for future work and education.
Abstract
Generative Artificial Intelligence (GenAI) has become an emerging technology with the availability of several tools that could impact Software Engineering (SE) activities. As any other disruptive technology, GenAI led to the speculation that its full potential can deeply change SE. However, an overfocus on improving activities for which GenAI is more suitable could negligent other relevant areas of the process. In this paper, we aim to explore which SE activities are not expected to be profoundly changed by GenAI. To achieve this goal, we performed a survey with SE practitioners to identify their expectations regarding GenAI in SE, including impacts, challenges, ethical issues, and aspects they do not expect to change. We compared our results with previous roadmaps proposed in SE literature. Our results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change, such as the need for human expertise, creativity, and project management. Our results point to SE areas for which GenAI is probably not so useful, and future research could tackle them to improve SE practice.
