PROBO is presented, an iterative approach that leverages JVM runtime metrics to guide Bayesian optimization for testing time reduction and generates candidate flag configurations through three complementary strategies guided by expected testing time improvement.
Code translation, as a challenging and fundamental task, is increasingly relying on large language models (LLMs). However, LLMs often give seemingly plausible but fallacious translations, misleading and even deceptive to debugging developers. We propose tHinter, an automated approach that frames translation error local...
Shengnan Wu, Xin-Yu Sun, Xin Wang et al.· ACM Transactions on Software...· 0 citations
EvoMem is introduced, a persistent memory architecture for LLM-based evolutionary program search that captures and reuses candidate mutation knowledge and provides evidence that persistent memory can reduce some redundant exploration and improve the reuse and adaptation of successful strategies in LLM-driven evolutiona...
Viktor Volkov, Valentin Khrulkov, Andrey V. Galichin et al.· 0 citations
The novel method of co-evolution labeling for predictive test optimization is introduced, deriving test relevance from tests and code changing together in the version history, which nearly matches the failure detection capabilities of failure-based models, while being more resistant to label noise and requiring no test...
Maximilian Jungwirth, RaphaelN ̈ommer, Andreas Stahlbauer et al.· 0 citations
CTForge is presented, an LLM-powered framework that automatically generates configuration-specific test suites and demonstrates that structured configuration-aware refinement is essential for LLM to produce effective test suites.
Yuanliang Zhang, Zhizheng Zheng, Shanshan Li et al.· 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
Reliable assessment of LLM-generated tests should treat executability as a gate and combine coverage with mutation testing and structural quality indicators, and in practice, model selection should precede prompt tuning.
Bilal Al-Ahmad, M. Harshvardhan, Khaled El-Fakih et al.· 0 citations
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