Review participation in modern code review

Thongtanunam, Patanamon; McIntosh, Shane; Hassan, Ahmed E.; Iida, Hajimu

doi:10.1007/s10664-016-9452-6

Cited by 96 publications

(64 citation statements)

References 49 publications

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“…In our models, the dependent variable is the top-k performance and the explanatory variables are the set of parameters outlined in Table 3. We fit our regression models using the Ordinary Least Squares (OLS) technique using the ols function provided by the rms package [51], as suggested by recent studies [52,53,54].…”

Section: Case Study Resultsmentioning

confidence: 99%

The impact of IR-based classifier configuration on the performance and the effort of method-level bug localization

Tantithamthavorn

Abebe

Hassan

et al. 2018

Information and Software Technology

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Context: IR-based bug localization is a classifier that assists developers in locating buggy source code entities (e.g., files and methods) based on the content of a bug report. Such IR-based classifiers have various parameters that can be configured differently (e.g., the choice of entity representation). Objective: In this paper, we investigate the impact of the choice of the IR-based classifier configuration on the top-k performance and the required effort to examine source code entities before locating a bug at the method level. Method: We execute a large space of classifier configuration, 3,172 in total, on 5,266 bug reports of two software systems, i.e., Eclipse and Mozilla. Results: We find that (1) the choice of classifier configuration impacts the top-k performance from 0.44% to 36% and the required effort from 4,395 to 50,000 LOC; (2) classifier configurations with similar top-k performance might require different efforts; (3) VSM achieves both the best top-k performance and the least required effort for method-level bug localization; (4) the likelihood of randomly picking a configuration that performs within 20% of the best top-k classifier configuration is on average 5.4% and that of the least effort is on average 1%; (5) configurations related to the entity representation of the analyzed data have the most impact on both the top-k performance and the required effort; and (6) the most efficient classifier configuration obtained at the method-level can also be used at the file-level (and vice versa). Conclusion: Our results lead us to conclude that configuration has a large impact on both the top-k performance and the required effort for method-level bug localization, suggesting that the IR-based configuration settings should be carefully selected and the required effort metric should be included in future bug localization studies.

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Section: Case Study Resultsmentioning

confidence: 99%

The impact of IR-based classifier configuration on the performance and the effort of method-level bug localization

Tantithamthavorn

Abebe

Hassan

et al. 2018

Information and Software Technology

Self Cite

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“…The (iii) Feature Extraction stage studies the overall perspectives in code review activities. We choose features based on prior work [19]. Table 2 describes each feature with its definition.…”

Section: Discussionmentioning

confidence: 99%

“…We measure the possible correlation among our features using Spearman's rank correlation coefficients (ρ) to remove highly correlated features. Similar to prior work [19], we use Spearman's rank correlation coefficient |ρ| = 0.7 as the threshold to eliminate highly Whether the purpose of a patch is documentation.…”

Section: Discussionmentioning

confidence: 99%

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Understanding Developer Commenting in Code Reviews

Hirao

Kula

Ihara

et al. 2019

IEICE Trans. Inf. & Syst.

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Toshiki HIRAO †a) , Raula GAIKOVINA KULA †b) , Nonmembers, Akinori IHARA † †c) , Member, and Kenichi MATSUMOTO †d) , Nonmember SUMMARY Modern code review is a well-known practice to assess the quality of software where developers discuss the quality in a web-based review tool. However, this lightweight approach may risk an inefficient review participation, especially when comments becomes either excessive (i.e., too many) or underwhelming (i.e., too few). In this study, we investigate the phenomena of reviewer commenting. Through a large-scale empirical analysis of over 1.1 million reviews from five OSS systems, we conduct an exploratory study to investigate the frequency, size, and evolution of reviewer commenting. Moreover, we also conduct a modeling study to understand the most important features that potentially drive reviewer comments. Our results find that (i) the number of comments and the number of words in the comments tend to vary among reviews and across studied systems; (ii) reviewers change their behaviours in commenting over time; and (iii) human experience and patch property aspects impact the number of comments and the number of words in the comments.

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“…Previous research has shown that code review, as a wellestablished software quality practice, is one of the most significant stages in pull-based development [17], [18], [23]. It ensures that only high-quality codes are accepted.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Review Comments in Pull-based Development Model

Li¹,

Yu²,

Yin³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-The pull-based model, widely used in distributed software development, allows any contributor to fork a public repository, package contributions as a pull-request, and then merge back to the original repository. Code review is one of the most significant stages in pull-based development. It ensures that only high-quality pull-requests are accepted, based on the in-depth discussion among reviewers. Thus, automatically identifying what reviewers are talking about in the discussions is benificial to better understand the code review process.In this paper, we conduct a case study on three popular opensource software projects hosted on GitHub and construct a finegrained taxonomy including 11 sub-categories for review comments. We then manually label over 5,600 review comments, and propose a Two-Stage Hybrid Classification (TSHC) algorithm to classify review comments automatically by combining rule-based and machine-learning techniques. Comparative experiments with a text-based method achieve a reasonable improvement on each project (9.2% in Rails, 5.3% in Elasticsearch, and 7.2% in Angular.js respectively) in terms of the weighted average Fmeasure.

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Review participation in modern code review

Cited by 96 publications

References 49 publications

The impact of IR-based classifier configuration on the performance and the effort of method-level bug localization

The impact of IR-based classifier configuration on the performance and the effort of method-level bug localization

Understanding Developer Commenting in Code Reviews

Automatic Classification of Review Comments in Pull-based Development Model

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