Titlebook: Generative AI for Effective Software Development; Anh Nguyen-Duc,Pekka Abrahamsson,Foutse Khomh Book 2024 The Editor(s) (if applicable) an

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developers by enabling them to work more efficiently, speed up the learning process, and increase motivation by reducing tedious and repetitive tasks. Moreover, our results indicate a change in teamwork collaboration due to software engineers using GenAI for help instead of asking coworkers, which impacts the learning loop in agile teams.

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Coefficients for Bivariate Relations,terview with them. Among the lessons learned are that the use of generative AI tools drives the adoption of additional developer tools and that developers intentionally use ChatGPT and Copilot in a complementary manner. We hope that sharing these practical experiences will help other software teams in successfully adopting generative AI tools.

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An Overview on Large Language ModelsLMs and augmented LLMs. Furthermore, we delve into the evaluation of LLM research, introducing benchmark datasets and relevant tools in this context. The chapter concludes by exploring limitations in leveraging LLMs for SE tasks.

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Advancing Requirements Engineering Through Generative AI: Assessing the Role of LLMsmprove the efficiency and accuracy of requirements-related tasks. We propose key directions and SWOT analysis for research and development in using LLMs for RE, focusing on the potential for requirements elicitation, analysis, specification, and validation. We further present the results from a preliminary evaluation, in this context.

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Generative AI for Software Development: A Family of Studies on Code Generationiscuss the potential pitfalls of using generative AI to perform such SE tasks, as well as the quality of the code generated by these models. Finally, we explore research opportunities in harnessing generative AI, with a particular emphasis on tasks that require code generation.

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