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Volume 18, Issue 2
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Aims

We propose a tool that can automatically generate datasets for software defect prediction from GitHub repositories.

Background

DevOps is a software development approach that emphasizes collaboration, communication, and automation in order to improve the speed and quality of software delivery.

Objective

This study aims to demonstrate the effectiveness of the tool, and in order to do so, a series of experiments were conducted on several popular GitHub repositories and compared the performance of our generated datasets with existing datasets.

Methods

The tool works by analyzing the commit history of a given repository and extracting relevant features that can be used to predict defects. These features include code complexity metrics, code churn, and the number of developers involved in a particular code change.

Results

Our results show that the datasets generated by our tool are comparable in quality to existing datasets and can be used to train effective software defect prediction models.

Conclusion

Overall, the proposed tool provides a convenient and effective way to generate high-quality datasets for software defect prediction, which can significantly improve the accuracy and reliability of prediction models.

© 2025 Bentham Science Publishers
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/content/journals/raeeng/10.2174/2352096516666230517155221
2023-06-05
2025-05-30

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/content/journals/raeeng/10.2174/2352096516666230517155221
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Improved DevOps Lifecycle by Integrating a Novel Tool V-Git Lab
Recent Advances in Electrical & Electronic Engineering 18, 196 (2025); https://doi.org/10.2174/2352096516666230517155221
/content/journals/raeeng/10.2174/2352096516666230517155221
/content/journals/raeeng/10.2174/2352096516666230517155221
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  • Article Type:     Research Article
Keyword(s): DevOps; Git-Hub; lifecycle; public repository; Software defect prediction; software engineering

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