Aug 2026· International Conference on Automated Software Engineering· Vol 33· 0 citations· 55 references
TL;DR
Results support a focused conclusion: LLM-generated review is most useful as complementary semantic guidance when paired with deployment-oriented test selection, rather than as a standalone testing artifact.
The results suggest that organizing real-bug mechanisms as retrievable and executable feedback targets is an effective way to guide generated tests toward bug-triggering inputs and behavioral oracles.
A dataset of LLM-generated broken-to-fixed test pairs is built, where initially failing tests are repaired via a loop under zero-shot, few-shot, and chain-of-thought prompting, and the performance of prompting strategies through repair rate and post-repair coverage is compared.
Recent work argues that multi-agent LLM frameworks generate better unit tests than a single well-crafted prompt by iteratively refining coverage and cross-checking oracles. We test this claim directly on a 25-class Java system, comparing CANDOR, a multi-agent framework with JaCoCodriven coverage feedback and panel-base...
These findings suggest that while AI agents excel at rigorous boundary testing, they lack the “environmental awareness” needed to write stable, hermetic tests.
Preet Jhanglani, Zeel Desai, Vidhi Kansara et al.· Annual International Compute...· 0 citations
JavaOracle is proposed, a specification-driven approach that leverages large language models to reason over specifications and systematically enhance test oracles and further automates test case minimization and bug report generation, producing submission-ready reports without manual effort.
Ruifeng Fu, Yingquan Zhao, Meng Wang et al.· SIGSOFT FSE Companion· 0 citations
TATG introduces a unified objective representation that captures testing requirements derived from static analysis and dynamic feedback that enables fine-grained tracking of satisfied and unresolved requirements throughout generation.
Guancheng Wang, Qinghua Xu, Lionel C. Briand· 0 citations
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