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1. Beyond the Code Itself: How Programmers Really Look at Pull Requests.

   Ford, Denae; Behroozi, Mahnaz; Serebrenik, Alexander; Parnin, Chris (January 2019, International Conference on Software Engineering, Software Engineering in Society Track)

   Developers in open source projects must make decisions on contributions from other community members, such as whether or not to accept a pull request. However, secondary factors—beyond the code itself—can influence those decisions. For example, signals from GitHub profiles, such as a number of followers, activity, names, or gender can also be considered when developers make decisions. In this paper, we examine how developers use these signals (or not) when making decisions about code contributions. To evaluate this question, we evaluate how signals related to perceived gender identity and code quality influenced decisions on accepting pull requests. Unlike previous work, we analyze this decision process with data collected from an eye-tracker. We analyzed differences in what signals developers said are important for themselves versus what signals they actually used to make decisions about others. We found that after the code snippet (x=57%), the second place programmers spent their time fixating on supplemental technical signals(x=32%), such as previous contributions and popular repositories. Diverging from what participants reported themselves, we also found that programmers fixated on social signals more than recalled. 

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2. How Developers and Tools Categorize Sentiment in Stack Overflow Questions - A Pilot Study

   https://doi.org/10.1109/SEmotion52567.2021.00012  

   Mansoor, Niloofar; Peterson, Cole S.; Sharif, Bonita (May 2021, 2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion))

   The paper presents results from a pilot questionnaire-based study on ten Stack Overflow (SO) questions. Eleven developers were tasked with determining if the SO question sentiment was positive, negative or neutral. The results from the questionnaire indicate that developers mostly rated the sentiment of SO questions as neutral, stating that they received little or no emotional feedback from the questions. Tools that were designed to analyze Software Engineering related texts (SentiStrength-SE, SentiCR, and Senti4SD) were on average more closely aligned with developer ratings for a majority of the questions than general purpose tools for detecting SO question sentiment. We discuss cases where tools and developer sentiment differ along with implications of the results. Overall, the sentiment tool output on the question title and body is more aligned with the developer rating than just the title alone. Since SO is a very common medium of technical exchange, we also report that adding code snippets, short titles, and multiple tags were top three features developers prefer in SO questions in order for it to be answered quickly. 

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3. Developer Reading Behavior while Summarizing Java Methods : Size and Context Matters

   Abid, N.; Sharif, B.; Dragan, N.; Alrasheed, H.; Maletic, J.I. (May 2019, ACM/IEEE International Conference on Software Engineering (ICSE))

   An eye-tracking study of 18 developers reading and summarizing Java methods is presented. The developers provide a written summary for methods assigned to them. In total, 63 methods are used from five different systems. Previous studies on this topic use only short methods presented in isolation usually as images. In contrast, this work presents the study in the Eclipse IDE allowing access to all the source code in the system. The developer can navigate via scrolling and switching files while writing the summary. New eye-tracking infrastructure allows for this improvement in the study environment. Data collected includes eye gazes on source code, written summaries, and time to complete each summary. Unlike prior work that concluded developers focus on the signature the most, these results indicate that they tend to focus on the method body more than the signature. Moreover, both experts and novices tend to revisit control flow terms rather than reading them for a long period. They also spend a significant amount of gaze time and have higher gaze visits when they read call terms. Experts tend to revisit the body of the method significantly more frequently than its signature as the size of the method increases. Moreover, experts tend to write their summaries from source code lines that they read the most. 

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4. Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

   Chen, Yuxiao; Yuan, Jianbo; You, Quanzeng; Luo, Jiebo (January 2018, Proceedings of ACM Multimedia Conference)

   Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis.We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments. 

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5. Influence of social and technical factors for evaluating contribution in GitHub

   https://doi.org/10.1145/2568225.2568315  

   Tsay, Jason; Dabbish, Laura; Herbsleb, James (January 2014, International Conference on Software Engineering)

   Open source software is commonly portrayed as a meritocracy, where decisions are based solely on their technical merit. However, literature on open source suggests a complex social structure underlying the meritocracy. Social work environments such as GitHub make the relationships between users and between users and work artifacts transparent. This transparency enables developers to better use information such as technical value and social connections when making work decisions. We present a study on open source software contribution in GitHub that focuses on the task of evaluating pull requests, which are one of the primary methods for contributing code in GitHub. We analyzed the association of various technical and social measures with the likelihood of contribution acceptance. We found that project managers made use of information signaling both good technical contribution practices for a pull request and the strength of the social connection between the submitter and project manager when evaluating pull requests. Pull requests with many comments were much less likely to be accepted, moderated by the submitter's prior interaction in the project. Well-established projects were more conservative in accepting pull requests. These findings provide evidence that developers use both technical and social information when evaluating potential contributions to open source software projects 

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