BUMP: A Benchmark of Reproducible Breaking Dependency Updates
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Abstract (arXiv)
Third-party dependency updates can cause a build to fail if the new dependency version introduces a change that is incompatible with the usage: this is called a breaking dependency update. Research on breaking dependency updates is active, with works on characterization, understanding, automatic repair of breaking updates, and other software engineering aspects. All such research projects require a benchmark of breaking updates that has the following properties: 1) it contains real-world breaking updates; 2) the breaking updates can be executed; 3) the benchmark provides stable scientific artifacts of breaking updates over time, a property we call reproducibility. To the best of our knowledge, such a benchmark is missing. To address this problem, we present BUMP, a new benchmark that contains reproducible breaking dependency updates in the context of Java projects built with the Maven build system. BUMP contains 571 breaking dependency updates collected from 153 Java projects. BUMP ensures long-term reproducibility of dependency updates on different platforms, guaranteeing consistent build failures. We categorize the different causes of build breakage in BUMP, providing novel insights for future work on breaking update engineering. To our knowledge, BUMP is the first of its kind, providing hundreds of real-world breaking updates that have all been made reproducible.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Jan 2024.
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Citing papers per year: 2025: 1; 2026: 4.
5 papers.
- 2025Specification and Detection of LLM Code Smellscited by 0
- 2026LLM Code Smells: A Taxonomy and Detection Approachcited by 0
- 2026Agentic Generation of AST Transformation Rules for Fixing Breaking Updatescited by 0
- 2026Update from Hell: Can Coding Agents Survive Hidden Breakage in Dependency Upgrades?cited by 0
- 2026Software World Models: From Consequence Prediction to Decision Valuecited by 0
- 2026Software World Models: From Consequence Prediction to Decision Value4 Oct 2026
- 2026Update from Hell: Can Coding Agents Survive Hidden Breakage in Dependency Upgrades?31 Aug 2026
- 2026Agentic Generation of AST Transformation Rules for Fixing Breaking Updates23 Jun 2026
- 2026LLM Code Smells: A Taxonomy and Detection Approach21 May 2026
- 2025Specification and Detection of LLM Code Smells19 Dec 2025