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Git Density: Analyze git repositories to extract the Source Code Density and other Commit Properties
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA;DISTA;DSIQ)ORCID iD: 0000-0001-7937-1645
2020 (English)Other (Other academic)
Resource type
Software, multimedia
Physical description [en]

Git Density is a software suite to analyze git-repositories with the goal of detecting the source code density and other properties of the software, such as metrics.

Abstract [en]

Git Density (git-density) is a tool to analyze git-repositories with the goal of detecting the source code density. It was developed during the research phase of the short technical paper and poster "A changeset-based approach to assess source code density and developer efficacy" and has since been extended to support thorough analyses and insights.

Place, publisher, year, pages
2020.
Keywords [en]
git, source code density, git-hours, software metrics
National Category
Computer Sciences Computer and Information Sciences
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-98140DOI: 10.5281/zenodo.2565238OAI: oai:DiVA.org:lnu-98140DiVA, id: diva2:1470045
Available from: 2020-09-23 Created: 2020-09-23 Last updated: 2023-09-28Bibliographically approved
In thesis
1. Quantifying Process Quality: The Role of Effective Organizational Learning in Software Evolution
Open this publication in new window or tab >>Quantifying Process Quality: The Role of Effective Organizational Learning in Software Evolution
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Real-world software applications must constantly evolve to remain relevant. This evolution occurs when developing new applications or adapting existing ones to meet new requirements, make corrections, or incorporate future functionality. Traditional methods of software quality control involve software quality models and continuous code inspection tools. These measures focus on directly assessing the quality of the software. However, there is a strong correlation and causation between the quality of the development process and the resulting software product. Therefore, improving the development process indirectly improves the software product, too. To achieve this, effective learning from past processes is necessary, often embraced through post mortem organizational learning. While qualitative evaluation of large artifacts is common, smaller quantitative changes captured by application lifecycle management are often overlooked. In addition to software metrics, these smaller changes can reveal complex phenomena related to project culture and management. Leveraging these changes can help detect and address such complex issues.

Software evolution was previously measured by the size of changes, but the lack of consensus on a reliable and versatile quantification method prevents its use as a dependable metric. Different size classifications fail to reliably describe the nature of evolution. While application lifecycle management data is rich, identifying which artifacts can model detrimental managerial practices remains uncertain. Approaches such as simulation modeling, discrete events simulation, or Bayesian networks have only limited ability to exploit continuous-time process models of such phenomena. Even worse, the accessibility and mechanistic insight into such gray- or black-box models are typically very low. To address these challenges, we suggest leveraging objectively captured digital artifacts from application lifecycle management, combined with qualitative analysis, for efficient organizational learning. A new language-independent metric is proposed to robustly capture the size of changes, significantly improving the accuracy of change nature determination. The classified changes are then used to explore, visualize, and suggest maintenance activities, enabling solid prediction of malpractice presence and -severity, even with limited data. Finally, parts of the automatic quantitative analysis are made accessible, potentially replacing expert-based qualitative analysis in parts.

Place, publisher, year, edition, pages
Växjö: Linnaeus University Press, 2023
Series
Linnaeus University Dissertations ; 504
Keywords
Software Size, Software Metrics, Commit Classification, Maintenance Activities, Software Quality, Process Quality, Project Management, Organizational Learning, Machine Learning, Visualization, Optimization
National Category
Computer and Information Sciences Software Engineering Mathematical Analysis Probability Theory and Statistics
Research subject
Computer Science, Software Technology; Computer Science, Information and software visualization; Computer and Information Sciences Computer Science, Computer Science; Statistics/Econometrics
Identifiers
urn:nbn:se:lnu:diva-124916 (URN)10.15626/LUD.504.2023 (DOI)9789180820738 (ISBN)9789180820745 (ISBN)
Public defence
2023-09-29, House D, D1136A, 351 95 Växjö, Växjö, 13:00 (English)
Opponent
Supervisors
Available from: 2023-09-28 Created: 2023-09-27 Last updated: 2024-05-06Bibliographically approved

Open Access in DiVA

git-density-release-2020.2(221 kB)0 downloads
File information
File name SOFTWARE01.zipFile size 221 kBChecksum SHA-512
b5e24ea07d47c93d79eefa780c06f0b4e6f211c309a886be70fca57dac8d933455b8e813e58bc1f6f320064e56bb6c7ca32bd1a21b881afbe3d6770bdd3768b0
Type softwareMimetype application/zip

Other links

Publisher's full textThe software on Zenodo.The software on GitHub.

Authority records

Hönel, Sebastian

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