Predicting Defectiveness of Software Patches

Soltanifar, Behjat; Erdem, Atakan; Bener, Ayşe

doi:10.1145/2961111.2962601

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…The method is an adaptation of an estimator (used in the formal software inspection process) to a scenario of iterative code review, where there are multiple review iterations. Soltanifar et al [116], in turn, proposed a prediction model similar to typical bug predictors, which builds a model to predict fault proneness based on a set of features. The difference of their approach is that they consider features associated with the review that has been done to predict whether a patch remains defective.…”

Section: Review Decisionmentioning

confidence: 99%

A Systematic Literature Review and Taxonomy of Modern Code Review

Davila,

Nunes

2021

Preprint

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Context: Modern Code Review (MCR) is a widely known practice of software quality assurance. However, the existing body of knowledge of MCR is currently not understood as a whole. Objective: Our goal is to identify the state of the art on MCR, providing a structured overview and an in-depth analysis of the research done in this field. Method: We performed a systematic literature review, selecting publications from four digital libraries. Results: A total of 139 papers were selected and analyzed in three main categories. Foundational studies are those that analyze existing or collected data from the adoption of MCR. Proposals consist of techniques and tools to support MCR, while evaluations are studies to assess an approach or compare a set of them. Conclusion: The most represented category is foundational studies, mainly aiming to understand the motivations for adopting MCR, its challenges and benefits, and which influence factors lead to which MCR outcomes. The most common types of proposals are code reviewer recommender and support to code checking. Evaluations of MCR-supporting approaches have been done mostly offline, without involving human subjects. Five main research gaps have been identified, which point out directions for future work in the area.

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Section: Review Decisionmentioning

confidence: 99%

A Systematic Literature Review and Taxonomy of Modern Code Review

Davila,

Nunes

2021

Preprint

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“…Few studies use change part granularity for prediction, so the features were partly adapted to fit our context. The studies selected for analysis are: (Menzies et al, 2010;Radjenović et al, 2013;Giger et al, 2012;Nagappan and Ball, 2005;Shihab et al, 2012;Ratzinger et al, 2007;Mockus and Weiss, 2000;McIntosh and Kamei, 2017;D'Ambros et al, 2010b;Arisholm et al, 2010;Soltanifar et al, 2016;Chen et al, 2017;Shivaji et al, 2013;Meneely et al, 2008;Shin et al, 2009;Eyolfson et al, 2011;Shihab, 2012;Osman and Nierstrasz, 2017).…”

Section: Feature Selectionmentioning

confidence: 99%

“…Results of just-in-time prediction at Google were disappointing (Lewis et al, 2013), but Tan et al (2015)'s case study at Cisco and especially Nayrolles and Hamou-Lhadj (2018)'s case study at Ubisoft show that an elaborated approach can bring just-in-time defect prediction to industrial usefulness. Further examples of defect prediction studies with change granularity are Kim et al (2008), Kamei et al (2013), Shivaji et al (2013), andSoltanifar et al (2016). Ray et al (2016) show that the entropy of code is related to its defect density and go down to the level of single lines.…”

Section: Related Workmentioning

confidence: 99%

An Industrial Case Study on Shrinking Code Review Changesets through Remark Prediction

Baum,

Herbold,

Schneider

2018

Preprint

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Change-based code review is used widely in industrial software development. Thus, research on tools that help the reviewer to achieve better review performance can have a high impact. We analyze one possibility to provide cognitive support for the reviewer: Determining the importance of change parts for review, specifically determining which parts of the code change can be left out from the review without harm. To determine the importance of change parts, we extract data from software repositories and build prediction models for review remarks based on this data. The approach is discussed in detail. To gather the input data, we propose a novel algorithm to trace review remarks to their triggers. We apply our approach in a medium-sized software company. In this company, we can avoid the review of 25% of the change parts and of 23% of the changed Java source code lines, while missing only about 1% of the review remarks. Still, we also observe severe limitations of the tried approach: Much of the savings are due to simple syntactic rules, noise in the data hampers the search for better prediction models, and some developers in the case company oppose the taken approach. Besides the main results on the mining and prediction of triggers for review remarks, we contribute experiences with a novel, multi-objective and interactive rule mining approach. The anonymized dataset from the company is made available, as are the implementations for the devised algorithms.

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