# SQuaD: The Software Quality Dataset

arXiv: https://arxiv.org/abs/2511.11265 · HTML: https://mlsanity.com/paper/2511.11265/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Why We Refactor? Confessions of GitHub Contributors](https://mlsanity.com/paper/1607.02459/) (2016)
- [An Extensive Comparison of Static Application Security Testing Tools](https://mlsanity.com/paper/2403.09219/) (2024)
- [The Technical Debt Dataset](https://mlsanity.com/paper/1908.00827/) (2019)
- [On the correlation between Architectural Smells and Static Analysis Warnings](https://mlsanity.com/paper/2406.17354/) (2024)
- [A Dataset of Software Bill of Materials for Evaluating SBOM Consumption Tools](https://mlsanity.com/paper/2504.06880/) (2025)
- [Different Debt: An Addition to the Technical Debt Dataset and a Demonstration Using Developer Personality](https://mlsanity.com/paper/2403.01157/) (2024)
- [Refactoring Detection in C++ Programs with RefactoringMiner++](https://mlsanity.com/paper/2502.17716/) (2025)

## Built on by (most cited first, 1 of 1)

- [AI Writes Code, Humans Pay the Debt. An Empirical Study on the Sustainability and Evolution of Agent-Generated Code](https://mlsanity.com/paper/2609.04208/) (2026)

## Abstract (arXiv)

Software quality research increasingly relies on large-scale datasets that measure both the product and process aspects of software systems. However, existing resources often focus on limited dimensions, such as code smells, technical debt, or refactoring activity, thereby restricting comprehensive analyses across time and quality dimensions. To address this gap, we present the Software Quality Dataset (SQuaD), a multi-dimensional, time-aware collection of software quality metrics extracted from 450 mature open-source projects across diverse ecosystems, including Apache, Mozilla, FFmpeg, and the Linux kernel. By integrating nine state-of-the-art static analysis tools, i.e., SonarQube, CodeScene, PMD, Understand, CK, JaSoMe, RefactoringMiner, RefactoringMiner++, and PyRef, our dataset unifies over 700 unique metrics at method, class, file, and project levels. Covering a total of 63,586 analyzed project releases, SQuaD also provides version control and issue-tracking histories, software vulnerability data (CVE/CWE), and process metrics proven to enhance Just-In-Time (JIT) defect prediction. The SQuaD enables empirical research on maintainability, technical debt, software evolution, and quality assessment at unprecedented scale. We also outline emerging research directions, including automated dataset updates and cross-project quality modeling to support the continuous evolution of software analytics. The dataset is publicly available on ZENODO (DOI: 10.5281/zenodo.17566690).