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ABSTRACTContextGenerative artificial intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAI's ChatGPT, GitHub Copilot, and Amazon CodeWhisperer.ObjectiveAlthough many recent publications have explored and evaluated the application of GenAI, a comprehensive understanding of the current development, applications, limitations, and open challenges remains unclear to many. Particularly, we do not have an overall picture of the current state of GenAI technology in practical software engineering usage scenarios.MethodWe conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for software engineering.ResultsWe identified 78 open research questions (RQs) in 11 areas of software engineering. Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision-making in all software development activities. While the current literature is skewed toward software implementation, quality assurance and software maintenance, other areas, such as requirements engineering, software design, and software engineering education, would need further research attention. Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology.ConclusionsGenAI is bringing significant changes to the field of software engineering. Nevertheless, the state of research on the topic still remains immature. We believe that this research agenda holds significance and practical value for informing both researchers and practitioners about current applications and guiding future research.

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Software startups develop new products or service using software development under extreme uncertainty conditions. Agile methodologies are considered the most suitable for these teams since they accomodate changes easier than traditional software development methodologies. A very well-known methodology for startups is Lean Startup that is considered to be a variant of agile methodologies. Nevertheless, prior studies found that Lean Startup adoption by software startups are still low. This research will focus to understand why the Lean Startup adoption is not higher despite its fame and history of good results. To achieve that, this research will try to develop a conceptual framework using determinants borrowed from technology adoption theories well-used in information systems literature and also on prior studies on agile methodology adoption. The conceptual framework will thenbe evaluated through a questionnaire with software startups practioners. It is expected that this conceptual framework could answer why more software startups are not using Lean Startup.