What is the connection between issues, bugs, and enhancements?

Krishna, Rahul; Agrawal, Amritanshu; Rahman, Akond; Sobran, Alexander; Menzies, Timothy

doi:10.1145/3183519.3183548

Cited by 30 publications

(8 citation statements)

References 51 publications

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“…There are two lines of work in empirical studies on issue reports. One line of work studied the discussions in issue reports [28]- [33], and the other co-related issue reports with project or issue success [34]- [37].…”

Section: Empirical Studies On Issue Discussion and Successmentioning

confidence: 99%

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An Empirical Study on Using Multi-Labels for Issues in GitHub

Kim

Lee

2021

IEEE Access

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On GitHub, one of the most successful services for software project hosting, labels have been used to represent various information and decisions about reported issues. However, previous studies on labels were limited to simple statistics or label recommendations for issue types. In this paper, we aim to provide a better understanding of labels and their usage in software development. We particularly focus on using multiple and custom labels on issues. To analyze label usage, we collected software project data and label usage information from GitHub. We then quantitatively investigated the performance of projects with multi-label features and qualitatively investigated the categories of multi-labels, and the usage of multilabels based on these categories. Our analysis results show that multi-labels are common in the majority of software projects and that projects using multi-label features manage their issues more effectively. In addition, our analysis results reveal different types of information represented by labels, which are related to features, development, and issues. This study finds several facts that can be used for studies on issue management and thus that help develop labeling techniques to mitigate the burden of issue management.

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Section: Empirical Studies On Issue Discussion and Successmentioning

confidence: 99%

“…Among the studies on the discussions in issue reports, Krishna et al worked on predicting the trends of bugs and enhancements based on the number of issue reports [28]. Hu et al studied the multiple discussions of developers across several issue reports [29].…”

Section: Empirical Studies On Issue Discussion and Successmentioning

confidence: 99%

An Empirical Study on Using Multi-Labels for Issues in GitHub

Kim

Lee

2021

IEEE Access

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“…GitHub, GitTorrent 4 ; and others 5 ) this should not be problematic. Consequently, it is now normal to publish using data extracted from dozens to hundreds of projects e.g., [61,3].…”

Section: Using Deprecated or Suspect Datamentioning

confidence: 99%

“Bad smells” in software analytics papers

Shepperd

2019

Information and Software Technology

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CONTEXT: There has been a rapid growth in the use of data analytics to underpin evidence-based software engineering. However the combination of complex techniques, diverse reporting standards and poorly understood underlying phenomena are causing some concern as to the reliability of studies. OBJECTIVE: Our goal is to provide guidance for producers and consumers of software analytics studies (computational experiments and correlation studies). METHOD: We propose using "bad smells", i.e., surface indications of deeper problems and popular in the agile software community and consider how they may be manifest in software analytics studies. RESULTS: We list 12 "bad smells" in software analytics papers (and show their impact by examples). CONCLUSIONS: We believe the metaphor of bad smell is a useful device. Therefore we encourage more debate on what contributes to the validty of software analytics studies (so we expect our list will mature over time).

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“…• Filter-1 (General): As the first step of filtering, we identify projects that contain sufficient software development information using the criteria used by prior research [1] [14]. By applying these filtering criteria we mitigate the limitations of mining GitHub projects stated by prior researchers [13] [5].…”

Section: Filteringmentioning

confidence: 99%

Characterizing the influence of continuous integration: empirical results from 250+ open source and proprietary projects

Rahman

Agrawal

Krishna

et al. 2018

Proceedings of the 4th ACM SIGSOFT International Workshop on Software Analytics

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Continuous integration (CI) tools integrate code changes by automatically compiling, building, and executing test cases upon submission of code changes. Use of CI tools is getting increasingly popular, yet how proprietary projects reap the benefits of CI remains unknown. To investigate the influence of CI on software development, we analyze 150 open source software (OSS) projects, and 123 proprietary projects. For OSS projects, we observe the expected benefits after CI adoption, e.g., improvements in bug and issue resolution. However, for the proprietary projects, we cannot make similar observations. Our findings indicate that only adoption of CI might not be enough to the improve software development process. CI can be effective for software development if practitioners use CI's feedback mechanism efficiently, by applying the practice of making frequent commits. For our set of proprietary projects we observe practitioners commit less frequently, and hence not use CI effectively for obtaining feedback on the submitted code changes. Based on our findings we recommend industry practitioners to adopt the best practices of CI to reap the benefits of CI tools for example, making frequent commits. CCS CONCEPTS• Software and its engineering → Agile software development;

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What is the connection between issues, bugs, and enhancements?

Cited by 30 publications

References 51 publications

An Empirical Study on Using Multi-Labels for Issues in GitHub

An Empirical Study on Using Multi-Labels for Issues in GitHub

“Bad smells” in software analytics papers

Characterizing the influence of continuous integration: empirical results from 250+ open source and proprietary projects

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