# Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy

arXiv: https://arxiv.org/abs/2411.08254 · HTML: https://mlsanity.com/paper/2411.08254/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 62 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 30 of 37)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models](https://mlsanity.com/paper/2310.04406/) (2023)
- [BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions](https://mlsanity.com/paper/2406.15877/) (2024)
- [CodeT: Code Generation with Generated Tests](https://mlsanity.com/paper/2207.10397/) (2022)
- [Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs](https://mlsanity.com/paper/2406.15927/) (2024)
- [LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations](https://mlsanity.com/paper/2410.02707/) (2024)
- [A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation](https://mlsanity.com/paper/2307.03987/) (2023)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification](https://mlsanity.com/paper/2403.04696/) (2024)
- [The Curious Case of Hallucinations in Neural Machine Translation](https://mlsanity.com/paper/2104.06683/) (2021)
- [Detecting Hallucinated Content in Conditional Neural Sequence Generation](https://mlsanity.com/paper/2011.02593/) (2020)
- [Unit Test Case Generation with Transformers and Focal Context](https://mlsanity.com/paper/2009.05617/) (2020)
- [No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation](https://mlsanity.com/paper/2305.04207/) (2023)
- [Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing](https://mlsanity.com/paper/2308.16557/) (2023)
- [Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach](https://mlsanity.com/paper/2405.19648/) (2024)
- [TESTEVAL: Benchmarking Large Language Models for Test Case Generation](https://mlsanity.com/paper/2406.04531/) (2024)
- [A3Test: Assertion-Augmented Automated Test Case Generation](https://mlsanity.com/paper/2302.10352/) (2023)
- [ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation](https://mlsanity.com/paper/2307.00588/) (2023)
- [Can Large Language Models Write Good Property-Based Tests?](https://mlsanity.com/paper/2307.04346/) (2023)
- [CAT-LM: Training Language Models on Aligned Code And Tests](https://mlsanity.com/paper/2310.01602/) (2023)
- [Large Language Models as Test Case Generators: Performance Evaluation and Enhancement](https://mlsanity.com/paper/2404.13340/) (2024)
- [Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation](https://mlsanity.com/paper/2310.02368/) (2023)
- [Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools](https://mlsanity.com/paper/2312.10622/) (2023)
- [Automatic Generation of Test Cases based on Bug Reports: a Feasibility Study with Large Language Models](https://mlsanity.com/paper/2310.06320/) (2023)
- [Assessing Evaluation Metrics for Neural Test Oracle Generation](https://mlsanity.com/paper/2310.07856/) (2023)

## Built on by (most cited first, 4 of 4)

- [Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning](https://mlsanity.com/paper/2502.13820/) (2025)
- [Judging Is Not Enumerating: Silent Omissions in LLM-Authored Acceptable Sets](https://mlsanity.com/paper/2608.01000/) (2026)
- [Prompt Coverage Adequacy](https://mlsanity.com/paper/2607.02057/) (2026)
- [Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)](https://mlsanity.com/paper/2607.05032/) (2026)

## Abstract (arXiv)

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 that semantic entropy is a reliable indicator to distinguish between valid and invalid test cases, which provides a robust solution for improving the correctness of LLM-generated test cases used in software testing and code generation.