Documentation Reuse: Hot or Not? An Empirical Study

Oumaziz, Mohamed A.; Charpentier, Alan; Falleri, Jean‐Rémy; Blanc, Xavier

doi:10.1007/978-3-319-56856-0_2

Cited by 14 publications

(10 citation statements)

References 26 publications

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“…Code documentation is a key component of software quality assurance. Good code documentation can greatly promote the understanding of software system, accelerate the process of learning and reusing code, increase developer productivity, simplify maintenance, and therefore improve the reliability of 1 http://github.com/sticeran/ProgramAndData software [25][26][27][28][29][30]. In contrast, poor code documentation is one of the main reasons for the rapid deterioration of software system quality [28].…”

Section: A Role Of Code Documentation In Software Quality Assurancementioning

confidence: 99%

“…ODE documentations play an important role in software quality assurance [1][2][3][4][5][6][25][26][27][28][29][30]. High quality code documentation is helpful for program comprehension [31][32][33], test case generation [55][56][57][58][59], API Recommendation [62][63][64][65][66][67], fault localization [5,[68][69][70][71][72][73][74], bug detection [77][78][79], and program repair [84].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prioritizing code documentation effort: Can we do it simpler but better?

Liu

Guo

et al. 2021

Information and Software Technology

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Section: A Role Of Code Documentation In Software Quality Assurancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prioritizing code documentation effort: Can we do it simpler but better?

Liu

Guo

et al. 2021

Information and Software Technology

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“…The works by Oumaziz et al [17] and Luciv et al [18] study what we call legitimate clones to encourage smart documentation reuse. Despite the different scope of these works compared to RepliComment, some of their findings are relevant for our research as well.…”

Section: Related Workmentioning

confidence: 99%

RepliComment: Identifying Clones in Code Comments

Blasi¹,

Stulova²,

Gorla³

et al. 2021

Preprint

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Code comments are the primary means to document implementation and facilitate program comprehension. Thus, their quality should be a primary concern to improve program maintenance. While much effort has been dedicated to detecting bad smells, such as clones in code, little work has focused on comments. In this paper we present our solution to detect clones in comments that developers should fix. RepliComment can automatically analyze Java projects and report instances of copy-and-paste errors in comments, and can point developers to which comments should be fixed. Moreover, it can report when clones are signs of poorly written comments. Developers should fix these instances too in order to improve the quality of the code documentation. Our evaluation of 10 well-known open source Java projects identified over 11K instances of comment clones, and over 1,300 of them are potentially critical. We improve on our own previous work, which could only find 36 issues in the same dataset. Our manual inspection of 412 issues reported by RepliComment reveals that it achieves a precision of 79% in reporting critical comment clones. The manual inspection of 200 additional comment clones that RepliComment filters out as being legitimate, could not evince any false negative.

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“…McIntosh et al [28] analysed 3,872 build systems looking for clones, they identified what are the underlying reasons behind their existence and which recent build technologies tend to be more prone to cloning. Oumaziz et al [29] performed an empirical study on documentation reuse. They applied a clustering technique (formal concept analysis) to detect duplicates in documentation.…”

Section: Related Workmentioning

confidence: 99%

Handling Duplicates in Dockerfiles Families: Learning from Experts

Oumaziz¹,

Falleri²,

Blanc³

et al. 2019

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

Self Cite

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Docker is becoming a popular tool used by developers and end-users to deploy and run software applications. Dockerfiles are now found alongside projects' source code. Several projects are even starting to maintain families of Dockerfiles, like the Python project that maintains a family of 43 Dockerfiles, each for a specific Python version on a specific Linux distribution. In some situations, Dockerfiles family maintainers have to propagate a change to several, if not all, Dockerfiles of the family (for instance a bugfix applying on all Dockerfiles targeting Python 2.7). This need to propagate changes is usually due to the presence of duplicates between several Dockerfiles of the family. In this paper, our goal is to provide practitioners a clear explanation for why Dockerfile duplicates arise in projects, and what are the different means to handle duplicates with their pros and cons. To perform a grounded analysis, we observe the practices of expert Dockerfile maintainers of Official Docker projects, which are setting the best-practices for Dockerfile maintenance. We show that duplicates in Dockerfiles are frequent in our corpus, that maintainers are aware of their existence, are frequently facing them and have a mixed opinion regarding them (error-prone when not using any tool, but easy to maintain with the right tools). Finally, we describe and analyse the tools used by maintainers to handle duplicates.

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Documentation Reuse: Hot or Not? An Empirical Study

Cited by 14 publications

References 26 publications

Prioritizing code documentation effort: Can we do it simpler but better?

Prioritizing code documentation effort: Can we do it simpler but better?

RepliComment: Identifying Clones in Code Comments

Handling Duplicates in Dockerfiles Families: Learning from Experts

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