An empirical study on the effect of community smells on bug prediction

Eken, Beyza; Palma, Francis; Başar, Ayşe; Tosun, Ayşe

doi:10.1007/s11219-020-09538-7

Cited by 10 publications

(8 citation statements)

References 47 publications

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“…We categorized 24 studies as empirical and one as theoretical according to Appendix A. Empirical studies conducted by Tamburri [5,11,20,42] and Palomba [10] offer information about the identification of the causes and effects of community smells in practice, information about the impact on work settings, and management approaches. The remaining included studies provide further information on community smells in relationship with development communities and processes [14,32,33,34,43,44,45,46] and research contributions such as models [12,13,16,47,48,49,50,51,52,53], organizational strategies [15], and tools [54]. We organized our results around the research questions described in Section 3.…”

Section: Reporting the Resultsmentioning

confidence: 99%

“…Prediction models for correlating community smells and code smells [10], predicting bug-prone code components [47], examining the impact of gender diversity in teams on mitigating community smells [12,16], automatically detecting community smells in opensource projects with high precision [48,49,50], and predicting community smells on software developers based on sentiment analysis [51, 52] Social Network Social-network-based models for building formal definitions of community smells [11] and quantify forms of social debt in software architecture activities [20] Continued on next page…”

Section: Statisticalmentioning

confidence: 99%

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Community smells—The sources of social debt: A systematic literature review

Caballero-Espinosa

Carver

Stowers³

2023

Information and Software Technology

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Section: Reporting the Resultsmentioning

confidence: 99%

Section: Statisticalmentioning

confidence: 99%

Community smells—The sources of social debt: A systematic literature review

Caballero-Espinosa

Carver

Stowers³

2023

Information and Software Technology

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“…The existing studies investigated the impact of community smell on technical artifacts such as code smell intensity [2] or bug [13] by employing a community-aware prediction model. Palomba et al [2] conducted an empirical study on nine open-source projects.…”

Section: A Missing Link Community Smellmentioning

confidence: 99%

“…They found that community smells contribute to the intensity of code smell. Eken et al [13] conducted an empirical investigation on ten open-source projects to find how community smells can predict bugs. The authors found the impact of community smells as a contributing factor in predicting bug-prone classes.…”

Section: A Missing Link Community Smellmentioning

confidence: 99%

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Do Missing Link Community Smell Affect Developers Productivity: An Empirical Study

2021

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Missing link smell occurs when developers contribute to the same source code without communicating with each other. Existing studies have analyzed the relationship of missing link smells with code smell and developer contribution. However, the productivity of developers involved in missing link smell has not been explored yet. This study investigates how productivity differs between smelly and non-smelly developers. For this purpose, the productivity of smelly and non-smelly developers of seven open-source projects are analyzed. The result shows that the developers not involved in missing link smell have more productivity than the developers involved in smells. The observed difference is also found statistically significant.

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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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An empirical study on the effect of community smells on bug prediction

Cited by 10 publications

References 47 publications

Community smells—The sources of social debt: A systematic literature review

Community smells—The sources of social debt: A systematic literature review

Do Missing Link Community Smell Affect Developers Productivity: An Empirical Study

Bug report priority prediction using social and technical features

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