An empirical investigation of how and why developers rename identifiers

Peruma, Anthony; Mkaouer, Mohamed Wiem; Decker, Michael John; Newman, Christian D.

doi:10.1145/3242163.3242169

Cited by 49 publications

(30 citation statements)

References 41 publications

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“…We aim to extend tsDetect to new types of test smells, such as those showing up in the naming practices of the test. This will leverage existing research on naming practices [5,11,19,[23][24][25] to study tests and determine when these practices are smelly. Finally, we encourage the community to download tsDetect and contribute to its improvement and extension.…”

Section: Discussionmentioning

confidence: 99%

tsDetect: an open source test smells detection tool

Peruma

Almalki

Newman

et al. 2020

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

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The test code, just like production source code, is subject to bad design and programming practices, also known as smells. The presence of test smells in a software project may affect the quality, maintainability, and extendability of test suites making them less effective in finding potential faults and quality issues in the project's production code. In this paper, we introduce tsDetect, an automated test smell detection tool for Java software systems that uses a set of detection rules to locate existing test smells in test code. We evaluate the effectiveness of tsDetect on a benchmark of 65 unit test files containing instances of 19 test smell types. Results show that tsDetect achieves a high detection accuracy with an average precision score of 96% and an average recall score of 97%. tsDetect is publicly available, with a demo video, at: https://testsmells.github.io/ CCS CONCEPTS • Software and its engineering → Software testing and debugging; Software notations and tools; Software maintenance tools.

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Section: Discussionmentioning

confidence: 99%

tsDetect: an open source test smells detection tool

Peruma

Almalki

Newman

et al. 2020

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

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“…Part-of-speech tagging is an important analysis technique for understanding the meaning of words found in identifiers and the relationships between these words. Numerous prior works rely on some form of part-of-speech tagging to draw conclusions from identifier names [19], [14], [36], [13], [12], [25], [37], [21], [22], [38], [39], [31]. Thus, improved part-ofspeech tagging technology can significantly improve the ability of researchers to understand the connection between identifier behavior and code behavior.…”

Section: Motivation and Related Workmentioning

confidence: 99%

An Ensemble Approach for Annotating Source Code Identifiers with Part-of-speech Tags

Newman,

Decker,

AlSuhaibani

et al. 2021

Preprint

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This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble's annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to promote the future amelioration of these problems through further research. Our results show that the ensemble achieves 75% accuracy at the identifier level and 84-86% accuracy at the word level. This is an increase of +17% points at the identifier level from the closest independent partof-speech tagger.

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“…As reported in the introduction, many studies have investigated software refactoring practices [32,37,39,43]. In this section, we focus on the ones aimed at investigating the impact of refactoring on code quality, since being the most related to our work.…”

Section: Related Workmentioning

confidence: 99%

“…Software refactoring has been extensively studied by the research community, through empirical studies investigating how and why developers perform refactoring [32,37,39,43,48,49], how refactoring relates with other development tasks (e.g., merge conflicts [35]), with software quality indicators (e.g., quality metrics) [5,17,45,46], and with developers' productivity [36]. Some studies (e.g., Kim et al.…”

Section: Introductionmentioning

confidence: 99%

On the relationship between refactoring actions and bugs: a differentiated replication

Penta

Bavota

Zampetti

2020

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

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Software refactoring aims at improving code quality while preserving the system's external behavior. Although in principle refactoring is a behavior-preserving activity, a study presented by Bavota et al. in 2012 reported the proneness of some refactoring actions (e.g., pull up method) to induce faults. The study was performed by mining refactoring activities and bugs from three systems. Taking profit of the advances made in the mining software repositories field (e.g., better tools to detect refactoring actions at commit-level granularity), we present a differentiated replication of the work by Bavota et al. in which we (i) overcome some of the weaknesses that affect their experimental design, (ii) answer the same research questions of the original study on a much larger dataset (3 vs 103 systems), and (iii) complement the quantitative analysis of the relationship between refactoring and bugs with a qualitative, manual inspection of commits aimed at verifying the extent to which refactoring actions trigger bug-fixing activities. The results of our quantitative analysis confirm the findings of the replicated study, while the qualitative analysis partially demystifies the role played by refactoring actions in the bug introduction. CCS CONCEPTS • Software and its engineering → Software reliability; Designing software.

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An empirical investigation of how and why developers rename identifiers

Cited by 49 publications

References 41 publications

tsDetect: an open source test smells detection tool

tsDetect: an open source test smells detection tool

An Ensemble Approach for Annotating Source Code Identifiers with Part-of-speech Tags

On the relationship between refactoring actions and bugs: a differentiated replication

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