publications

2026

1. arXiv

   Who Broke Me? Execution-Guided Repair of Behavioral Dependency Breaks

   Juntao Chen, Tingting Bi, Yanlin Wang, and Patanamon Thongtanunam

   arXiv preprint arXiv:2610.09267, Oct 2026

   Abs arXiv PDF

   Dependency upgrades can break downstream projects without changing the library interface. Such behavioral breaking changes are difficult for developers to fix, because the failing test does not always point to the root API, the upstream API that causes the break. Existing LLM-based repair methods obtain evidence for the repair from compiler feedback or library documentation. However, a behavioral break produces no compiler feedback and is often undocumented. Agents that receive no upgrade evidence also usually do not retrieve upstream evidence themselves, and most of their failed repairs do not identify the root API. The library diff provides useful upstream evidence, but the root API must first be identified to select the relevant part of the diff. We present BBCFixer, a repair method that runs the failing test under the old and new library versions, ranks the calls whose return value differs to identify a candidate root API, and filters the library diff with that candidate. To evaluate repairs of behavioral breaks, we further introduce BBCBench, a benchmark of 100 behavioral dependency breaks in Python and JavaScript. We compare BBCFixer on BBCBench with two baselines: (1) Pure Agent, an agent that receives no upgrade evidence, and (2) BDUpdater, a method that mines library documentation. BBCFixer raises the average pass rate by up to 17% relative to Pure Agent and needs up to 27% fewer agent steps per successful repair. BBCFixer thus provides a way to select the upstream evidence behind a behavioral break, so that an LLM agent can repair behavioral breaking changes more effectively and efficiently.

2. arXiv

   Breaking Changes in Software Ecosystems: A Systematic Literature Review

   Juntao Chen, Tingting Bi, Yanlin Wang, and Patanamon Thongtanunam

   arXiv preprint arXiv:2605.24397, May 2026

   Abs arXiv PDF

   Modern software systems rely on dependency networks of reusable libraries, where breaking changes propagate and cause downstream consumers to fail. Despite growing research across ecosystems, no comprehensive synthesis exists. We conduct a systematic literature review of 97 primary studies, answering four research questions across five ecosystems: Maven/Java, npm/JavaScript, Python, Web APIs, and Linux distributions. The synthesis yields four results. First, a four-dimensional taxonomy along Nature, Detectability, Scope, and Visibility. Second, five reason categories and five impact dimensions, where maintenance and design improvements account for a larger share of breaking changes than new feature work. Third, 43 detection approaches that reach high accuracy on syntactic breaks but limited coverage on behavioral ones. Fourth, 66 strategies for communicating, preventing, and recovering from breaking changes, organized by the actor's role. Based on these findings, we identify three open challenges and three research opportunities.