Developer Behavior and Sentiment from Data Mining Open Source Repositories

Robinson, William; Deng, Tianjie; Qi, Zirun

doi:10.1109/hicss.2016.465

Cited by 10 publications

(9 citation statements)

References 51 publications

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“…Robinson et al [12] performed data analysis on open source projects looking to understand how behavior change can change developers sentiment, and they analyzed 2 points, behavior change and routine change and their relationship with sentiment change. They used a regression model to search for the relations ship between developer sentiment change and developer behavior change.…”

Section: Related Workmentioning

confidence: 99%

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Analyzing The Impact Of Feedback In GitHub On The Software Developer's Mood

Santos

Caetano

Oliveira

et al. 2018

International Conferences on Software Engineering and Knowledge Engineering

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Software development depends on cooperation between people, and the way it works can define the future of the software project. Developers emotions affect their productivity and way they work, yet there is little information about how developers can influence the mood of each other. As a first step toward understanding how feedback may affect the developers' sentiment, this paper analyzes the mood variations on more than 78k pull requests and 268k pull comments on GitHub. We found that in 31.16% of the cases the developers presented a significant mood variation within one hour when receiving feedback on their pull requests. The variation reduces to 18.16% when evaluating one day before and after the commentary. In software projects with less than 34k lines of code, the number of developers that never contribute again after receiving a negative comment on the first pull request is 10.97%; this number more than doubles to 24.02% when evaluating projects with more than 197k lines of code.

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Section: Related Workmentioning

confidence: 99%

“…Recently, researchers have published several papers, regarding developer sentiment analysis, on GitHub interactions [5,7,8,9,12,14]. However, what causes positive or negative variances in the developers sentiment, as well as the duration of the variation, is not entirely understood yet; many external influencing factors remain unexplored.…”

Section: Introductionmentioning

confidence: 99%

Analyzing The Impact Of Feedback In GitHub On The Software Developer's Mood

Santos

Caetano

Oliveira

et al. 2018

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…For example, William [1] showed some developer behaviors and sentiments from open source repository mining; Christoph [2] argued that how do programmers ask ad answer questions on StackOverflow. On the other hand, Few people studied the association between these two communities.…”

Section: B Mining Developer Behaviors On Software Communitiesmentioning

confidence: 99%

“…As developers continuously contribute to or exchange ideas through these communities, a lot of development data and knowledge are accumulated there. According to the data from ghtorrent 1 and archive.org 2 , there are more than 20 million repositories and 5 million developers in GitHub, 30 million posts and 6 million users in StackOverflow as of August 2016. It is a great opportunity to understand characteristics and working habits of software developers through analyzing and mining these data.…”

Section: Introductionmentioning

confidence: 99%

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Mining Developer Behavior Across GitHub and StackOverflow

Xiong

Meng

Shen

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Nowadays, software developers are increasingly involved in GitHub and StackOverflow, creating a lot of valuable data in the two communities. Researchers mine the information in these software communities to understand developer behaviors, while previous work mainly focuses on mining data within a single community. In this paper, we propose a novel approach to mining developer behaviors across GitHub and StackOverflow. This approach links the accounts from two communities using a CART decision tree, leveraging the features from usernames, user behaviors and writing styles. Then, it explores cross-site developer behaviors through T-graph analysis, LDA-based topics clustering and cross-site tagging. We conducted several experiments to evaluate this approach. The results show that the precision and F-Score of our identity linkage method are higher than previous methods in software communities. Especially, we discovered that (1) active issue committers are also active question askers; (2) for most developers, the topics of their contents in GitHub are similar to that of their questions and answers in StackOverflow; (3) developers' concerns in StackOverflow shift over the time of their current participating projects in GitHub; (4) developers' concerns in GitHub are more relevant to their answers than questions and comments in StackOverflow.

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Bug report priority prediction using social and technical features

Huang,

Shao,

Fan

et al. 2023

J Software Evolu Process

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SummarySoftware stakeholders report bugs in issue tracking system (ITS) with manually labeled priorities. However, the lack of knowledge and standard for prioritization may cause stakeholders to mislabel the priorities. In response, priority predictors are actively developed to support them. Prior studies trained machine learners based on textual similarity, categorical, and numeric technical features of bug reports. Most models were validated by time‐insensitive approaches, and they were producing suboptimal results for practical usage. While they ignored the social aspects of ITS, the technical aspects were also limited in surface features of bug reports. To better model the bug report, we extract their topic and most similar code structures. Since ITS bridges users and developers as the main contributors, we also integrate their experience, sentiment, and socio‐technical features to construct a new dataset. Then, we perform two‐classed and multiclassed bug priority prediction based on the dataset. We also introduce adversarial training using generated training data with random word swap and random word deletion. We validate our model in within‐project, cross‐project, and time‐wise scenarios, and it outperforms the two baselines by up to 15% in area under curve‐receiver operating characteristics (AUC‐ROC) and 19% in Matthews correlation coefficient (MCC). We reveal involving contributor (i.e., assignee and reporter) features such as sentiment that could boost prediction performance. Finally, we test statistically the mean and distribution of the features that reflect the differences in social and technical aspects (e.g., quality of communication and resource distribution) between high and low priority reports. In conclusion, we suggest that researchers should consider both social and technical aspects of ITS in bug report priority prediction and introduce adversarial training to boost model performance.

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Developer Behavior and Sentiment from Data Mining Open Source Repositories

Cited by 10 publications

References 51 publications

Analyzing The Impact Of Feedback In GitHub On The Software Developer's Mood

Analyzing The Impact Of Feedback In GitHub On The Software Developer's Mood

Mining Developer Behavior Across GitHub and StackOverflow

Bug report priority prediction using social and technical features

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