  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, 38K 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.
