Context Generative AI tools can enhance the productivity and effectiveness of software development. These tools are evolving rapidly, as are the associated risks. Therefore, software organizations adopting these tools need to understand their risks, why they occur, and how they evolve over time. Objective Previous research has highlighted risks related to using generative AI in software development; however, the underlying causes of these risks remain largely unexplored. To effectively manage these risks, we need to understand what causes them. Therefore, we examine the causal explanations behind the risks of using generative AI in software development organizations. Methods In a multi-case study of three Danish software organizations during the early stages of generative AI tool adoption, we interviewed software developers and managers within these organizations to uncover the causal explanations of risks associated with generative AI. We employed a causal mapping approach to understand and visualize the differences and similarities across software organizations’ causal explanations of risks. A year later, we returned to validate the risks and causal maps with representatives from each software organization. Results Our study shows that the software organizations exhibit circular reasoning regarding a perpetual uncertainty in the validity of AI-generated code, with the three risks: Mistrust of AI, Insufficient validation of AI output , and Insufficient/insecure AI output. They are also concerned about similar root risks, which solely cause other risks without being caused by any risks themselves, such as Lacking AI competencies . While they differed in emphasis on tail-end risks, which are understood as not causing any additional risks. Conclusion The causal mapping approach proved appropriate for showing similarities and differences in software organizations’ perceptions of risks and causal explanations related to the use of generative AI in software development. The same applies to visualizing how the emphasis on risks can change over time.

Accountability is an innate part of social systems. It maintains stability and ensures positive pressure on individuals’ decision-making. As actors in a social system, software developers are accountable to their team and organization for their decisions. However, the drivers of accountability and how it changes behavior in software development are less understood. In this study, we look at how the social aspects of code review affect software engineers’ sense of accountability for code quality. Since Software Engineering (SE) is increasingly involving Large Language Models (LLM) assistance, we also evaluate the impact on accountability when introducing LLM-assisted code reviews. We carried out a two-phased sequential qualitative study ( \(\textbf{interviews}\rightarrow\textbf{focus groups}\) ). In Phase I (16 interviews), we sought to investigate the intrinsic drivers of software engineers influencing their sense of accountability for code quality, relying on self-reported claims. In Phase II, we tested these traits in a more natural setting by simulating traditional peer-led reviews with focus groups and then LLM-assisted review sessions. We found that there are four key intrinsic drivers of accountability for code quality: personal standards , professional integrity , pride in code quality , and maintaining one’s reputation . In a traditional peer-led review, we observed a transition from individual to collective accountability when code reviews are initiated. We also found that the introduction of LLM-assisted reviews disrupts this accountability process, challenging the reciprocity of accountability taking place in peer-led evaluations, i.e., one cannot be accountable to an LLM. Our findings imply that the introduction of AI into SE must preserve social integrity and collective accountability mechanisms.

Increasingly, research projects narrate visions of energy communities that portray hopes of more sustainable, democratic energy futures. However, it remains unarticulated how such research narratives are embedded in the design of digital technology for communal energy futures that are situated in everyday life. While sustainable HCI has identified relevant design narratives, little attention has been paid to those of communal energy projects. In this paper, we scope energy community literature at ACM to identify design narratives that tell stories about how energy communities are imagined and why they are relevant. Through a critical discourse analysis, we describe how design narratives currently shape energy community research on sites, participation, and digital technologies. We use these stories to discuss and suggest three trajectories of how future HCI researchers and practitioners may explore alternative and sustainable visions of energy community futures.