Categorizing the Content of GitHub README Files

Prana, Gede Artha Azriadi; Treude, Christoph; Thung, Ferdian; Atapattu, Thushari; Lo, David

doi:10.1007/s10664-018-9660-3

Cited by 91 publications

(74 citation statements)

References 65 publications

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“…Our future research will focus on the ways in which guideline documents, such as README files [9], can assist new developers. In particular, it is not clear to what extent the mandatory use of CI tools improves the process of contributing code to a new project.…”

Section: Discussionmentioning

confidence: 99%

“…They explicitly mention: "We recommend every repository include a README, LICEN SE, and .gitignore." And while README files do not necessarily give the impression of something that contains contribution guidelines, Prana et al [9] demonstrate that they usually do. Additionally, as mentioned previously, GitHub actively reminds contributors of the existence of contribution guidelines and suggests they be inspected before making a contribution [4].…”

Section: A Github Contribution Guidelinesmentioning

confidence: 99%

“…Prana et al [9] manually coded 393 README.md files and built a machine learning model that predicts the category a certain text would fall under, such as which part of the guidelines refers to who, what and why of the contribution process. They do not consider if these guidelines are followed nor do they provide details on the contribution process itself.…”

Section: A Github Contribution Guidelinesmentioning

confidence: 99%

“…• The guideline file for a project is too small; less than 2 KB of data, similar to the filtering criteria used by Prana et al [9]. • The project guideline file contains no actual guidelines, rather it is only a link to an external source (typically style guides for particular languages) 2 .…”

Section: A Project Selection Criteriamentioning

confidence: 99%

“…The limitations from our work include generalizability, in that we were limited to mining the workflow data from only 53 projects of the candidate 72 projects we considered in this research. Our coding process may also be subject to bias, which we mitigated by referencing previous work on contribution guidelines [9].…”

Section: Threats To Validitymentioning

confidence: 99%

See 4 more Smart Citations

Do as I Do, Not as I Say: Do Contribution Guidelines Match the GitHub Contribution Process?

Elazhary

Storey

Ernst

et al. 2019

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

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Developer contribution guidelines are used in social coding sites like GitHub to explain and shape the process a project expects contributors to follow. They set standards for all participants and "save time and hassle caused by improperly created pull requests or issues that have to be rejected and resubmitted" (GitHub). Yet, we lack a systematic understanding of the content of a typical contribution guideline, as well as the extent to which these guidelines are followed in practice. Additionally, understanding how guidelines may impact projects that use Continuous Integration as part of the contribution process is of particular interest. To address this knowledge gap, we conducted a mixed-methods study of 53 GitHub projects with explicit contribution guidelines and coded the guidelines to extract key themes. We then created a process model using GitHub activity data (e.g., commit, new issue, new pull request) to compare the actual activity with the prescribed contribution guidelines. We show that approximately 68% of these projects diverge significantly from the expected process.

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Section: Discussionmentioning

confidence: 99%

Section: A Github Contribution Guidelinesmentioning

confidence: 99%

Section: A Github Contribution Guidelinesmentioning

confidence: 99%

Section: A Project Selection Criteriamentioning

confidence: 99%

Section: Threats To Validitymentioning

confidence: 99%

See 3 more Smart Citations

Do as I Do, Not as I Say: Do Contribution Guidelines Match the GitHub Contribution Process?

Elazhary

Storey

Ernst

et al. 2019

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

View full text Add to dashboard Cite

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CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Ullah

Naeem

et al. 2022

Int J of Intelligent Sys

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Software similarity in different programming codes is a rapidly evolving field because of its numerous applications in software development, software cloning, software plagiarism, and software forensics. Currently, software researchers and developers search cross‐language open‐source repositories for similar applications for a variety of reasons, such as reusing programming code, analyzing different implementations, and looking for a better application. However, it is a challenging task because each programming language has a unique syntax and semantic structure. In this paper, a novel tool called Cross‐Language Software Similarity (CroLSSim) is designed to detect similar software applications written in different programming codes. First, the Abstract Syntax Tree (AST) features are collected from different programming codes. These are high‐quality features that can show the abstract view of each program. Then, Methods Description (MDrep) in combination with AST is used to examine the relationship among different method calls. Second, the Term Frequency Inverse Document Frequency approach is used to retrieve the local and global weights from AST‐MDrep features. Third, the Latent Semantic Analysis‐based features extraction and selection method is proposed to extract the semantic anchors in reduced dimensional space. Fourth, the Convolution Neural Network (CNN)‐based features extraction method is proposed to mine the deep features. Finally, a hybrid deep learning model of CNN‐Long‐Short‐Term Memory is designed to detect semantically similar software applications from these latent variables. The data set contains approximately 9.5K Java, 8.8K C#, and 7.4K C++ software applications obtained from GitHub. The proposed approach outperforms as compared with the state‐of‐the‐art methods.

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Qualitative software engineering research: Reflections and guidelines

Lenberg,

Feldt,

Gren

et al. 2023

J Software Evolu Process

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Researchers are increasingly recognizing the importance of human aspects in software development. Because qualitative methods are used to explore human behavior in‐depth, we believe that studies using such methods will become more common. Existing qualitative software engineering guidelines do not cover the full breadth of qualitative methods and the knowledge on how to use them like in social sciences. The purpose of this study was to extend the software engineering community's current body of knowledge regarding available qualitative methods and their quality assurance frameworks and to provide recommendations and guidelines for their use. With the support of an epistemological argument and a survey of the literature, we suggest that future research would benefit from (1) utilizing a broader set of research methods, (2) more strongly emphasizing reflexivity, and (3) employing qualitative guidelines and quality criteria. We present an overview of three qualitative methods commonly used in social sciences but rarely seen in software engineering research, namely interpretative phenomenological analysis, narrative analysis, and discourse analysis. Furthermore, we discuss the meaning of reflexivity in relation to the software engineering context and suggest means of fostering it. Our paper will help software engineering researchers better select and then guide the application of a broader set of qualitative research methods.

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Categorizing the Content of GitHub README Files

Cited by 91 publications

References 65 publications

Do as I Do, Not as I Say: Do Contribution Guidelines Match the GitHub Contribution Process?

Do as I Do, Not as I Say: Do Contribution Guidelines Match the GitHub Contribution Process?

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Qualitative software engineering research: Reflections and guidelines

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