Dataset of open-source software developers labeled by their experience level in the project and their associated software metrics
Developers are extracted from 17 open-source projects from GitHub. Projects are chosen that use the java programming language, the Spring framework and Maven/Gradle build tools. Along with these developers, 24 software engineering metrics are extracted for each of them. These metrics are either calc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340922010459 |
| Summary: | Developers are extracted from 17 open-source projects from GitHub. Projects are chosen that use the java programming language, the Spring framework and Maven/Gradle build tools. Along with these developers, 24 software engineering metrics are extracted for each of them. These metrics are either calculated by analyzing the source code or relative to project management metadata. Each of these developers then are manually searched for in professional social media such as LinkedIn or Twitter to be labeled with their experience level in their project. Outliers are statistically detected and manually re-assigned when needed. The resulting dataset contains 703 anonymized developers qualified by their 24 project-related software engineering metrics and labeled for their experience. It is suitable for empirical software engineering studies that need to connect developers’ level of experience to tangible software engineering metrics. |
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| ISSN: | 2352-3409 |