Employing Source Code Quality Analytics for Enriching Code Snippets Data

Thomas Karanikiotis (), Themistoklis Diamantopoulos () and Andreas Symeonidis
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Thomas Karanikiotis: Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Themistoklis Diamantopoulos: Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Andreas Symeonidis: Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece

Data, 2023, vol. 8, issue 9, 1-12

Abstract: The availability of code snippets in online repositories like GitHub has led to an uptick in code reuse, this way further supporting an open-source component-based development paradigm. The likelihood of code reuse rises when the code components or snippets are of high quality, especially in terms of readability, making their integration and upkeep simpler. Toward this direction, we have developed a dataset of code snippets that takes into account both the functional and the quality characteristics of the snippets. The dataset is based on the CodeSearchNet corpus and comprises additional information, including static analysis metrics, code violations, readability assessments, and source code similarity metrics. Thus, using this dataset, both software researchers and practitioners can conveniently find and employ code snippets that satisfy diverse functional needs while also demonstrating excellent readability and maintainability.

Keywords: mining software repositories; source code mining; readability; static analysis metrics; code snippets (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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