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.
Abstract
Large Language Model (LLM) pipelines can generate semantically meaningful unit tests with high coverage, but generated tests often fail to compile or execute. Many frameworks therefore use LLM-driven repair loops that iteratively re-prompt on failures until validation succeeds, but these repairs can introduce issues such as hallucinating an invented class solely to make the test pass. In this paper, we study repair-induced issues and effects of prompt strategy across three Java projects. We build a dataset of LLM-generated broken-to-fixed test pairs, where initially failing tests are repaired via a loop under zero-shot, few-shot, and chain-of-thought prompting, derive a taxonomy from 117 manually analyzed pairs (three anti-pattern categories, seven scenarios), and compare the performance of prompting strategies through repair rate and post-repair coverage.
This paper addresses automated unit test generation with large language models (LLMs). LLM-based test generation has not yet attained a quality level sufficient for practical use in industry. Although LLMs often reproduce API syntax faithfully, they frequently disregard semantic usage constraints and execution-environm...
This paper identifies patch verbosity as a major yet overlooked concern in LLM-based APR and proposes RECAP, a lightweight, plug-and-play adapter that attaches to existing repair frameworks after generation that achieves a substantially better size-correctness tradeoff.
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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.
It is shown that prompt engineering "ages" in a model-family-specific way: Newer GPT models exhibit diminishing or even negative marginal gains from structured prompting, suggesting that instruction-following and reasoning scaffolds are increasingly internalized, whereas Qwen models continue to benefit substantially fr...
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Research on LLM-based code generation has achieved strong performance on function-level benchmarks, yet repository-level code generation in real-world projects still faces challenges from cross-file dependencies. Existing studies usually emphasize single-round or final-round outcomes, which limits the analysis of where...
Fuze Kuang, Dongmei Liu, Yong-Li Wang et al.· International Conference on...· 0 citations
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.
Hui-Xiang Zhen, Zhihan Zhang· International Conference on...· 0 citations
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