Overview of Mining Software Repositories

Luzgin, Victor A.; Kholod, Ivan

doi:10.1109/eiconrus49466.2020.9039225

Cited by 3 publications

(3 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To enable an initial evaluation of the proposed approach, we implemented a proof-ofconcept capable of capturing repositories from the GitHub platform. GitHub is the most famous platform of open source code and, since 2016, it has already over 10 million Git repositories which should be enough for the range of the search [7,5]. Moreover, GitHub has the GitHub API V4 with GraphQL, which is used to collect the repositories from GitHub the database.…”

Section: Actual Statementioning

confidence: 99%

Tracking the decisions to select repositories for Mining Software Repositories experiments

Costa,

Oliveira,

Salles

et al. 2024

Anais Estendidos Do XX Simpósio Brasileiro De Sistemas De Informação (SBSI 2024)

View full text Add to dashboard Cite

Mining Software Repositories analyzes and cross-links the data available in software repositories. This enables MRS to recognize patterns in software repositories. For example, to study how developers resolve conflicting merges. However, two main problems exist in the selection process of repositories: the limitations presented in traditional approaches used when selecting repositories and the lack of a systematic process for choosing repositories, turning off the experiments' reproducibility. This approach is proposed to resolve identified limitations and assist users in software repositories' selection. Initial results show that this approach returns at least 1.8 times more repositories, overcoming, for instance, the main language restriction in searches.

show abstract

Section: Actual Statementioning

confidence: 99%

Tracking the decisions to select repositories for Mining Software Repositories experiments

Costa,

Oliveira,

Salles

et al. 2024

Anais Estendidos Do XX Simpósio Brasileiro De Sistemas De Informação (SBSI 2024)

View full text Add to dashboard Cite

show abstract

“…We first identified a set of software projects from the high energy physics domain (see Table 1) available on the github repository [15]. So far we have selected 167 projects.…”

Section: Dataset Constructionmentioning

confidence: 99%

“…Code constitutes only a small part of software repositories that usually include issue descriptions, history of code changes with their comments, various logs an other unstructured data. Mining Software Repositories (MSR) is the data-mining based activity that can provide new insights about how software has been developed and maintained, and consequently limit its defect proneness, improve maintainability reducing its change-proneness [15].…”

Section: Introductionmentioning

confidence: 99%

Using Natural Language Processing to Extract Information from Unstructured code-change version control data: lessons learned

Ronchieri¹,

Canaparo²,

Yang³

2021

Proceedings of International Symposium on Grids &Amp; Clouds 2021 — PoS(ISGC2021)

View full text Add to dashboard Cite

Context: Natural Language Processing (NLP) is a branch of artificial intelligence that extracts information from language. In the field of software engineering, NLP has been employed to extract key information from free-form text, to generate models from the analysis of text or to categorize code changes according to their commit messages. In literature, most of the approaches NLP-based focused on the impact of code changes on program execution or software architecture. Objective: In this study, we have applied NLP to code-change data to identify patterns of software code modifications and used Machine Learning techniques to build a model that determines how software has evolved over time and identifies area of code that presents problems. Method: Considering that software projects use version control systems, such as github, to manage their code, we have collected software information by using git commands. These data contain different unstructured information about the various files in a project. Each modification entry includes a message that explains the reasons for the change. According to the content of the message, it is possible to identify key terms that can be used during the classification of the entries. Results: In this study, we have considered the change history of software available on github to the High Energy Physics community. With the use of NLP techniques we have cleaned the messages and extracted some key terms to categorize both software problems and some other changes performed by developers, like the addition of a third party dependency or a script that starts a given service. We have built a code change dictionary combining the terms already in existing literature with the ones gathered directly from the software and its github repository. Finally, we have applied some Machine Learning (ML) techniques to determine any connection between code changes and software problems: we have removed redundant entries to avoid any bias in the outcomes of the ML techniques. Conclusion: We show in detail our approach adopted to construct historical code change datasets of categorized commit messages by following a multi-label classification methodology. Our model performance seems promising in terms of accuracy, precision, recall and F1-score.

show abstract

A Mining Software Repository Extended Cookbook: Lessons learned from a literature review

Barros¹,

Horita²,

Wiese³

et al. 2021

Preprint

View full text Add to dashboard Cite

Overview of Mining Software Repositories

Cited by 3 publications

References 66 publications

Tracking the decisions to select repositories for Mining Software Repositories experiments

Tracking the decisions to select repositories for Mining Software Repositories experiments

Using Natural Language Processing to Extract Information from Unstructured code-change version control data: lessons learned

A Mining Software Repository Extended Cookbook: Lessons learned from a literature review

Contact Info

Product

Resources

About