LLM-based Low-Level Integration Test Generation for Java

[2605.26851] LLM-based Low-Level Integration Test Generation for Java Skip to main content Search Submit Donate Log in Computer Science > Software Engineering arXiv:2605.26851 (cs) [Submitted on 26 May 2026 ( v1 ), last revised 22 Aug 2026 (this version, v2)] Title: LLM-based Low-Level Integration Test Generation for Java Authors: Qinghua Xu , Guancheng Wang , Lionel Briand , Zhaoqiang Guo , Kui Liu View PDF HTML (experimental) Abstract: Large language models (LLMs) show promise for automated test generation, but most approaches target unit tests with mocked dependencies. Low-level integration testing instead exercises a class with its real, in-project dependencies, exposing faults involving object construction, API call sequences, and component interactions. Generating such tests is challenging because LLMs may lack project-specific knowledge (not knowing) or violate provided constraints (not following). We present IntTestGen, an LLM-based approach that combines context-enriched generation with constraint-enforced fixing. It mines dependency usage patterns from project code to guide test generation, then applies symbol-, protocol-, and iteration-level constraints during repair using a ClassIndex, a Markov typestate model, and experience memory. We evaluate IntTestGen against the state-of-the-art LLM-based baseline PANTA and search-based baseline EvoSuite on Defects4J and Deps4J, a new post-cutoff benchmark of recent Java repositories. Across the two benchmarks, IntTestGen improves line coverage by 19.99 and 22.69 percentage points, branch coverage by 24.90 and 15.78 points, and mutation score by 13.67 and 0.17 points, respectively. It also covers 378 and 55 additional lines of dependency code. Ablation results confirm that all major components contribute to performance. Subjects: Software Engineering (cs.SE) Cite as: arXiv:2605.26851 [cs.SE] (or arXiv:2605.26851v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2605.26851 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Qinghua Xu [ view email ] [v1] Tue, 26 May 2026 11:08:04 UTC (1,220 KB) [v2] Sat, 22 Aug 2026 08:07:56 UTC (1,273 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.SE < prev | next > new | recent | 2026-05 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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