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[2411.08254] Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy
Summary
The paper presents VALTEST, a framework for validating test cases generated by large language models (LLMs) using semantic entropy, improving test validity and code generation performance.
Why It Matters
As LLMs are increasingly used in software development, ensuring the validity of their generated test cases is crucial. VALTEST addresses the challenge of invalid or hallucinated test cases, which can hinder the performance of programming agents. This research contributes to enhancing the reliability of automated testing processes, thereby improving software quality.
Key Takeaways
- VALTEST improves the validity of LLM-generated test cases by up to 29%.
- The framework uses semantic entropy to classify test cases as valid or invalid.
- Enhanced test validity leads to significant improvements in code generation performance.
- Semantic entropy serves as a reliable indicator for distinguishing test case validity.
- The research provides a robust solution for improving LLM-generated test cases in software testing.
Computer Science > Software Engineering arXiv:2411.08254 (cs) [Submitted on 13 Nov 2024 (v1), last revised 25 Feb 2026 (this version, v3)] Title:Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy Authors:Hamed Taherkhani, Jiho Shin, Muhammad Ammar Tahir, Md Rakib Hossain Misu, Vineet Sunil Gattani, Hadi Hemmati View a PDF of the paper titled Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy, by Hamed Taherkhani and 5 other authors View PDF Abstract:Modern Large Language Model (LLM)-based programming agents often rely on test execution feedback to refine their generated code. These tests are synthetically generated by LLMs. However, LLMs may produce invalid or hallucinated test cases, which can mislead feedback loops and degrade the performance of agents in refining and improving code. This paper introduces VALTEST, a novel framework that leverages semantic entropy to automatically validate test cases generated by LLMs. Analyzing the semantic structure of test cases and computing entropy-based uncertainty measures, VALTEST trains a machine learning model to classify test cases as valid or invalid and filters out invalid test cases. Experiments on multiple benchmark datasets and various LLMs show that VALTEST not only boosts test validity by up to 29% but also improves code generation performance, as evidenced by significant increases in pass@1 scores. Our extensive experiments also reveal tha...
