DARTS.7.2.13.pdf
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CodeDJ: Reproducible Queries over Large-Scale Software Repositories (Artifact)
Authors
Petr Maj 
,
Konrad Siek¹ ,
Alexander Kovalenko ,
Jan Vitek
-
Part of:
Issue:
Special Issue of the 35th European Conference on Object-Oriented Programming (ECOOP 2021)
Volume: DARTS, Volume 7 (ECOOP 2021)
Conference: European Conference on Object-Oriented Programming (ECOOP)
Journal: Dagstuhl Artifacts Series (DARTS) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2021-07-06
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Petr Maj, Konrad Siek¹, Alexander Kovalenko, and Jan Vitek. CodeDJ: Reproducible Queries over Large-Scale Software Repositories (Artifact). In Special Issue of the 35th European Conference on Object-Oriented Programming (ECOOP 2021). Dagstuhl Artifacts Series (DARTS), Volume 7, Issue 2, pp. 13:1-13:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/DARTS.7.2.13
https://doi.org/10.4230/DARTS.7.2.13
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DARTS-7-2-13-artifact-84f3bd083b289355e20112dd26cf5887.tgz
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Abstract
Analyzing massive code bases is a staple of modern software engineering research – a welcome side-effect of the advent of large-scale software repositories such as GitHub. Selecting which projects one should analyze is a labor-intensive process, and a process that can lead to biased results if the selection is not representative of the population of interest. One issue faced by researchers is that the interface exposed by software repositories only allows the most basic of queries. CodeDJ is an infrastructure for querying repositories composed of a persistent datastore, constantly updated with data acquired from GitHub, and an in-memory database with a Rust query interface. CodeDJ supports reproducibility, historical queries are answered deterministically using past states of the datastore; thus researchers can reproduce published results. To illustrate the benefits of CodeDJ, we identify biases in the data of a published study and, by repeating the analysis with new data, we demonstrate that the study’s conclusions were sensitive to the choice of projects.
Subject Classification
ACM Subject Classification
- Software and its engineering → Ultra-large-scale systems
Keywords
- Software
- Mining Code Repositories
- Source Code Analysis
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Total Accesses (updated on a weekly basis)
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References
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