Source Code Summarization is an emerging technology for automatically generating brief descriptions of code. Current summarization techniques work by selecting a subset of the statements and keywords from the code, and then including information from those statements and keywords in the summary. The quality of the summary depends heavily on the process of selecting the subset: a high-quality selection would contain the same statements and keywords that a programmer would choose. Unfortunately, little evidence exists about the statements and keywords that programmers view as important when they summarize source code. In this paper, we present an eye-tracking study of 10 professional Java programmers in which the programmers read Java methods and wrote English summaries of those methods. We apply the findings to build a novel summarization tool. Then, we evaluate this tool and provide evidence to support the development of source code summarization systems.

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Source Code Summarization is an emerging technology for automatically generating brief descriptions of code. Current summarization techniques work by selecting a subset of the statements and keywords from the code, and then including information from those statements and keywords in the summary. The quality of the summary depends heavily on the process of selecting the subset: a high-quality selection would contain the same statements and keywords that a programmer would choose. Unfortunately, little evidence exists about the statements and keywords that programmers view as important when they summarize source code. In this paper, we present an eye-tracking study of 10 professional Java programmers in which the programmers read Java methods and wrote English summaries of those methods. We apply the findings to build a novel summarization tool. Then, we evaluate this tool. Finally, we further analyze the programmers' method summaries to explore specific keyword usage and provide evidence to support the development of source code summarization systems.

is paper targets the problem of speech act detection in conversations about bug repair. We conduct a "Wizard of Oz" experiment with 30 professional programmers, in which the programmers x bugs for two hours, and use a simulated virtual assistant for help. en, we use an open coding manual annotation procedure to identify the speech act types in the conversations. Finally, we train and evaluate a supervised learning algorithm to automatically detect the speech act types in the conversations. In 30 two-hour conversations, we made 2459 annotations and uncovered 26 speech act types. Our automated detection achieved 69% precision and 50% recall. e key application of this work is to advance the state of the art for virtual assistants in so ware engineering. Virtual assistant technology is growing rapidly, though applications in so ware engineering are behind those in other areas, largely due to a lack of relevant data and experiments. is paper targets this problem in the area of developer Q/A conversations about bug repair. CCS CONCEPTS•So ware and its engineering → Maintaining so ware;

The mass shift to working at home during the COVID-19 pandemic radically changed the way many software development teams collaborate and communicate. To investigate how team culture and team productivity may also have been affected, we conducted two surveys at a large software company. The first, an exploratory survey during the early months of the pandemic with 2,265 developer responses, revealed that many developers faced challenges reaching milestones and that their team productivity had changed. We also found through qualitative analysis that important team culture factors such as communication and social connection had been affected. For example, the simple phrase "How was your weekend?" had become a subtle way to show peer support.In our second survey, we conducted a quantitative analysis of the team cultural factors that emerged from our first survey to understand the prevalence of the reported changes. From 608 developer responses, we found that 74% of these respondents missed social interactions with colleagues and 51% reported a decrease in their communication ease with colleagues. We used data from the second survey to build a regression model to identify important team culture factors for modeling team productivity. We found that the ability to brainstorm with colleagues, difficulty communicating with colleagues, and satisfaction with interactions from social activities are important factors that are associated with how developers report their software development team's productivity. Our findings inform how managers and leaders in large software companies can support sustained team productivity during times of crisis and beyond.

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