SPARK improves LLM-based test code fault localization by retrieving similar past faults and selectively annotating suspicious lines in new failing tests.
Y ang et al., An empirical study of unit test generation with large language models , arXiv preprint arXiv:2406.18181 (2024)
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A static-plus-dynamic analysis technique extracts isolated unit tests from integration tests to improve test suite structure in Node.js projects.
AdaDec improves Pass@1 accuracy of LLM code generation by up to 20.9% over greedy decoding by triggering lookahead reranking only at high-uncertainty steps on HumanEval+, MBPP+, and DevEval.
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Similar Pattern Annotation via Retrieval Knowledge for LLM-Based Test Code Fault Localization
SPARK improves LLM-based test code fault localization by retrieving similar past faults and selectively annotating suspicious lines in new failing tests.
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Augmenting unit test suites from integration tests
A static-plus-dynamic analysis technique extracts isolated unit tests from integration tests to improve test suite structure in Node.js projects.
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AdaDec: A Uncertainty-Guided Lookahead Decoding Framework for LLM-Based Code Generation
AdaDec improves Pass@1 accuracy of LLM code generation by up to 20.9% over greedy decoding by triggering lookahead reranking only at high-uncertainty steps on HumanEval+, MBPP+, and DevEval.