2026-07-23T00:29:18Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:14049 2024-03-06T10:53:10Z ddc:004 open_access

CodeDJ: Reproducible Queries over Large-Scale Software Repositories Maj, Petr Siek, Konrad Kovalenko, Alexander Vitek, Jan Software Mining Code Repositories Source Code Analysis 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Petr Maj and Konrad Siek and Alexander Kovalenko and Jan Vitek 2021 Is Part Of LIPIcs, Volume 194, 35th European Conference on Object-Oriented Programming (ECOOP 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ECOOP.2021.6 urn:nbn:de:0030-drops-140498 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2021.6 eng https://creativecommons.org/licenses/by/4.0/legalcode