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Documents authored by Maj, Petr

Document
DOI: 10.4230/LIPIcs.ECOOP.2024.27
The Fault in Our Stars: Designing Reproducible Large-scale Code Analysis Experiments

Authors: Petr Maj, Stefanie Muroya, Konrad Siek, Luca Di Grazia, and Jan Vitek

Published in: LIPIcs, Volume 313, 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Abstract
Large-scale software repositories are a source of insights for software engineering. They offer an unmatched window into the software development process at scale. Their sheer number and size holds the promise of broadly applicable results. At the same time, that very size presents practical challenges for scaling tools and algorithms to millions of projects. A reasonable approach is to limit studies to representative samples of the population of interest. Broadly applicable conclusions can then be obtained by generalizing to the entire population. The contribution of this paper is a standardized experimental design methodology for choosing the inputs of studies working with large-scale repositories. We advocate for a methodology that clearly lays out what the population of interest is, how to sample it, and that fosters reproducibility. Along the way, we discourage researchers from using extrinsic attributes of projects such as stars, that measure some unclear notion of popularity.
Cite as

Petr Maj, Stefanie Muroya, Konrad Siek, Luca Di Grazia, and Jan Vitek. The Fault in Our Stars: Designing Reproducible Large-scale Code Analysis Experiments. In 38th European Conference on Object-Oriented Programming (ECOOP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 313, pp. 27:1-27:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{maj_et_al:LIPIcs.ECOOP.2024.27,
  author =	{Maj, Petr and Muroya, Stefanie and Siek, Konrad and Di Grazia, Luca and Vitek, Jan},
  title =	{{The Fault in Our Stars: Designing Reproducible Large-scale Code Analysis Experiments}},
  booktitle =	{38th European Conference on Object-Oriented Programming (ECOOP 2024)},
  pages =	{27:1--27:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-341-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{313},
  editor =	{Aldrich, Jonathan and Salvaneschi, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2024.27},
  URN =		{urn:nbn:de:0030-drops-208769},
  doi =		{10.4230/LIPIcs.ECOOP.2024.27},
  annote =	{Keywords: software, mining code repositories, source code analysis}
}
Document
Artifact
DOI: 10.4230/DARTS.7.2.13
CodeDJ: Reproducible Queries over Large-Scale Software Repositories (Artifact)

Authors: Petr Maj, Konrad Siek¹, Alexander Kovalenko, and Jan Vitek

Published in: DARTS, Volume 7, Issue 2, Special Issue of the 35th European Conference on Object-Oriented Programming (ECOOP 2021)

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.
Cite as

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)

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@Article{maj_et_al:DARTS.7.2.13,
  author =	{Maj, Petr and Siek¹, Konrad and Kovalenko, Alexander and Vitek, Jan},
  title =	{{CodeDJ: Reproducible Queries over Large-Scale Software Repositories (Artifact)}},
  pages =	{13:1--13:4},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2021},
  volume =	{7},
  number =	{2},
  editor =	{Maj, Petr and Siek¹, Konrad and Kovalenko, Alexander and Vitek, Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.7.2.13},
  URN =		{urn:nbn:de:0030-drops-140371},
  doi =		{10.4230/DARTS.7.2.13},
  annote =	{Keywords: Software, Mining Code Repositories, Source Code Analysis}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2021.6
CodeDJ: Reproducible Queries over Large-Scale Software Repositories

Authors: Petr Maj, Konrad Siek, Alexander Kovalenko, and Jan Vitek

Published in: LIPIcs, Volume 194, 35th European Conference on Object-Oriented Programming (ECOOP 2021)

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.
Cite as

Petr Maj, Konrad Siek, Alexander Kovalenko, and Jan Vitek. CodeDJ: Reproducible Queries over Large-Scale Software Repositories. In 35th European Conference on Object-Oriented Programming (ECOOP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 194, pp. 6:1-6:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{maj_et_al:LIPIcs.ECOOP.2021.6,
  author =	{Maj, Petr and Siek, Konrad and Kovalenko, Alexander and Vitek, Jan},
  title =	{{CodeDJ: Reproducible Queries over Large-Scale Software Repositories}},
  booktitle =	{35th European Conference on Object-Oriented Programming (ECOOP 2021)},
  pages =	{6:1--6:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-190-0},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{194},
  editor =	{M{\o}ller, Anders and Sridharan, Manu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2021.6},
  URN =		{urn:nbn:de:0030-drops-140498},
  doi =		{10.4230/LIPIcs.ECOOP.2021.6},
  annote =	{Keywords: Software, Mining Code Repositories, Source Code Analysis}
}
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