The Python Software Quality Dataset

With Python's ascension as a dominant program-ming language, particularly in the fields of artificial intelligence and data science, the need for comprehensive datasets focusing on software quality within Python projects has become increasingly noticeable. This study introduces a detailed datas...

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Vydané v:Proceedings (EUROMICRO Conference on Software Engineering and Advanced Applications. Online) s. 395 - 398
Hlavní autori: Moldovan, Vasilica-Andreea, Berciu, Liviu-Marian, Patcas, Rares-Danut
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 28.08.2024
Predmet:
Computer bugs
Correlation
Data science
Focusing
Github Mining
Measurement
Python
Python Dataset
Refactoring
Software development management
Software engineering
Software Metrics
Software quality
Software reliability
SonarQube Mining
SZZ
ISSN:2376-9521
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Shrnutí:With Python's ascension as a dominant program-ming language, particularly in the fields of artificial intelligence and data science, the need for comprehensive datasets focusing on software quality within Python projects has become increasingly noticeable. This study introduces a detailed dataset designed to address this gap, enriching academic resources in software engineering. The dataset encompasses a wide array of software quality metrics on up to 80 projects, including 51.765.853 Sonar-Qube issues, 268.506 SonarQube code quality metrics, 11.915 software refactoring records, and 155.127 pairs of bug-inducing and bug-fixing commits, along with 863.931 GitHub issue tracker entries. This extensive collection serves as a versatile tool for various research activities, enabling analysis of the relationships between technical debt and software refactorings, correlations be-tween refactoring processes and bug resolution, and their overall impact on software maintainability and reliability. By offering a comprehensive and multifaceted dataset, this study significantly contributes to understanding and improving software quality in Python projects.
ISSN:2376-9521
DOI:10.1109/SEAA64295.2024.00066