TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

The Technical Debt Dataset

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoPROMISE'19
AlaotsikkoProceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering
KustantajaACM
Sivut2-11
ISBN (elektroninen)978-1-4503-7233-6
DOI - pysyväislinkit
TilaJulkaistu - syyskuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Predictive Models and Data Analytics in Software Engineering - Porto de Galinhas, Brasilia
Kesto: 18 syyskuuta 2019 → …

Conference

ConferenceInternational Conference on Predictive Models and Data Analytics in Software Engineering
MaaBrasilia
KaupunkiPorto de Galinhas
Ajanjakso18/09/19 → …

Tiivistelmä

Technical Debt analysis is increasing in popularity as nowadays researchers and industry are adopting various tools for static code analysis to evaluate the quality of their code. Despite this, empirical studies on software projects are expensive because of the time needed to analyze the projects. In addition, the results are difficult to compare as studies commonly consider different projects. In this work, we propose the Technical Debt Dataset, a curated set of project measurement data from 33 Java projects from the Apache Software Foundation. In the Technical Debt Dataset, we analyzed all commits from separately defined time frames with SonarQube to collect Technical Debt information and with Ptidej to detect code smells. Moreover, we extracted all available commit information from the git logs, the refactoring applied with Refactoring Miner, and fault information reported in the issue trackers (Jira). Using this information, we executed the SZZ algorithm to identify the fault-inducing and -fixing commits. We analyzed 78K commits from the selected 33 projects, detecting 1.8M SonarQube issues, 62K code smells, 28K faults and 57K refactorings. The project analysis took more than 200 days. In this paper, we describe the data retrieval pipeline together with the tools used for the analysis. The dataset is made available through CSV files and an SQLite database to facilitate queries on the data. The Technical Debt Dataset aims to open up diverse opportunities for Technical Debt research, enabling researchers to compare results on common projects.

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