CodeDJ: Reproducible queries over large-scale software repositories

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00354096" target="_blank" >RIV/68407700:21240/21:00354096 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.4230/LIPIcs.ECOOP.2021.6" target="_blank" >https://doi.org/10.4230/LIPIcs.ECOOP.2021.6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4230/LIPIcs.ECOOP.2021.6" target="_blank" >10.4230/LIPIcs.ECOOP.2021.6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

CodeDJ: Reproducible queries over large-scale software repositories

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

CodeDJ: Reproducible queries over large-scale software repositories

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF15_003%2F0000421" target="_blank" >EF15_003/0000421: Big Code: Škálovatelná analýza rozsáhlých bází programů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Leibniz International Proceedings in Informatics (LIPIcs)

ISBN

978-3-95977-190-0

ISSN

—

e-ISSN

1868-8969

Počet stran výsledku

24

Strana od-do

1-24

Název nakladatele

Dagstuhl Publishing,

Místo vydání

Saarbrücken

Místo konání akce

online

Datum konání akce

11. 7. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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