LLM-based methods achieve higher F1-scores than traditional approaches for equivalent mutant detection in Java and C, with fine-tuned code embeddings performing best and showing cross-lingual generalization.
On the evaluation of large language models in unit test generation
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LLM workflow with library-aware doubles and iterative compile-repair produces compilable unit tests for 73 of 76 OpenSIL functions and reaches 98.8% line coverage on a guided subset.
SPARK improves LLM-based test code fault localization by retrieving similar past faults and selectively annotating suspicious lines in new failing tests.
EvoSuite produced at least one fail-to-pass test for 36% of PRs versus 13% for GPT-4o, but both tools generated no meaningful change-capturing tests for 64% of the PRs evaluated.
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.
citing papers explorer
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Large Language Models for Multi-Lingual Equivalent Mutant Detection: An Extended Empirical Study
LLM-based methods achieve higher F1-scores than traditional approaches for equivalent mutant detection in Java and C, with fine-tuned code embeddings performing best and showing cross-lingual generalization.
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Library-Aware Doubles and Iterative Repair for Large Language Model-Generated Unit Tests in OpenSIL Firmware
LLM workflow with library-aware doubles and iterative compile-repair produces compilable unit tests for 73 of 76 OpenSIL functions and reaches 98.8% line coverage on a guided subset.
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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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PR-Aware Automated Unit Test Generation: Challenges and Opportunities
EvoSuite produced at least one fail-to-pass test for 36% of PRs versus 13% for GPT-4o, but both tools generated no meaningful change-capturing tests for 64% of the PRs evaluated.
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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.
- Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics