Locating Latent Design Information in Developer Discussions: A Study on Pull Requests

Viviani, Giovanni; Famelis, Michalis; Xia, Xin; Janik-Jones, Calahan; Murphy, Gail C.

doi:10.1109/tse.2019.2924006

Cited by 25 publications

(32 citation statements)

References 53 publications

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“…The studies of Sharma et al [22], Bhat et al [3], Brennan et al [7], Nassif et al [17] and Viviani et al [31] are similar to what is proposed to answer our RQ2. They identified knowledge in textual data from developer communication, through a classification technique.…”

Section: Related Worksupporting

confidence: 81%

Reusing software engineering knowledge from developer communication

Silva

2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Section: Related Worksupporting

confidence: 81%

Reusing software engineering knowledge from developer communication

Silva

2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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“…Ernst and Murphy [29] used this case study approach to analyze how requirements and design were discussed in open-source projects. One follow-up to this work is that of Viviani, [27], [11], papers which focus on rubrics for identifying design discussions. The advantage to the qualitative approach is that it can use more nuance in labeling design discussions at more specific level; the tradeoff of course is such labeling is labourintensive.…”

Section: B Mining Design Discussionmentioning

confidence: 99%

“…This subjectivity makes analyzing design decisions difficult [4]. Researchers have looked for ways in which design discussions could be automatically extracted from different types of software artifacts [5], [6], [7], [8], [9], [10], [11]. This automatic extraction, which we call design mining, is a subset of research based on mining software repositories.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Dataset Design Discussion Mining

Mahadi

Tongay

Ernst

2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Being able to identify software discussions that are primarily about design-which we call design mining-can improve documentation and maintenance of software systems. Existing design mining approaches have good classification performance using natural language processing (NLP) techniques, but the conclusion stability of these approaches is generally poor. A classifier trained on a given dataset of software projects has so far not worked well on different artifacts or different datasets. In this study, we replicate and synthesize these earlier results in a meta-analysis. We then apply recent work in transfer learning for NLP to the problem of design mining. However, for our datasets, these deep transfer learning classifiers perform no better than less complex classifiers. We conclude by discussing some reasons behind the transfer learning approach to design mining.

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“…Besides software changes, researchers have studied the automatic summarization of other software artifacts, such as source code [26], [30], [45]- [52], bug reports [53]- [56], app reviews [57], developer discussions [58]- [60] and development activity [61]. Concerning source code, some techniques have been proposed to summarize source code based on program analysis and manually-defined templates [45], [47], [49], information retrieval [46], [48], and learning-based methods [26], [30], [50].…”

Section: Summarizing and Documenting Other Software Artifactsmentioning

confidence: 99%

Automatic Generation of Pull Request Descriptions

Liu

Xia

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Enabled by the pull-based development model, developers can easily contribute to a project through pull requests (PRs). When creating a PR, developers can add a free-form description to describe what changes are made in this PR and/or why. Such a description is helpful for reviewers and other developers to gain a quick understanding of the PR without touching the details and may reduce the possibility of the PR being ignored or rejected. However, developers sometimes neglect to write descriptions for PRs. For example, in our collected dataset with over 333K PRs, more than 34% of the PR descriptions are empty. To alleviate this problem, we propose an approach to automatically generate PR descriptions based on the commit messages and the added source code comments in the PRs. We regard this problem as a text summarization problem and solve it using a novel sequence-to-sequence model. To cope with out-of-vocabulary words in software artifacts and bridge the gap between the training loss function of the sequence-tosequence model and the evaluation metric ROUGE, which has been shown to correspond to human evaluation, we integrate the pointer generator and directly optimize for ROUGE using reinforcement learning and a special loss function. We build a dataset with over 41K PRs and evaluate our approach on this dataset through ROUGE and a human evaluation. Our evaluation results show that our approach outperforms two baselines by significant margins.

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Locating Latent Design Information in Developer Discussions: A Study on Pull Requests

Cited by 25 publications

References 53 publications

Reusing software engineering knowledge from developer communication

Reusing software engineering knowledge from developer communication

Cross-Dataset Design Discussion Mining

Automatic Generation of Pull Request Descriptions

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