MineValiCoder: Reliable Code Generation with Test Case Quality Mining and Bipartite Graph-Based Mutual Validation

• URL: http://arxiv.org/abs/2607.22471v1
• Date: Fri, 24 Jul 2026 16:39:06 GMT
• Title: MineValiCoder: Reliable Code Generation with Test Case Quality Mining and Bipartite Graph-Based Mutual Validation
• Abstract summary: We propose a closed-loop Test-Driven Development (TDD) framework based on the mutual reinforcement of code quality and case quality.<n>MineValiCoder significantly outperforms state-of-the-art methods.
• Score: 9.901505812242652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Model (LLM)-based Test-Driven Development (TDD) has advanced automated code generation. However, existing approaches depend heavily on human-crafted test cases and cannot operate effectively when only natural-language requirements are available. Although recent work enables automatic test generation, it often overlooks the inherent stochasticity of LLMs, leading to two key defects: faulty tests generate misleading feedback that distorts code optimization, while mixed-quality test cases produce conflicting evaluation signals that hinder reliable code selection. To address these challenges, we propose MineValiCoder, a collaborative closed-loop TDD framework based on the mutual reinforcement of test-case quality and code quality. MineValiCoder comprises three modules. The Test Case Quality Mining (TCQM) module filters faulty test cases through self-validation, providing reliable optimization supervision. The Parallel TDD Refinement module iteratively optimizes code and generates diverse high-quality code candidates using validated test-case feedback. The Bipartite Graph-Based Code-Test Mutual Validation (BiCoTeV) module dynamically models code-test interactions and performs mutual validation scoring for stable and reliable optimal-code selection. Extensive evaluations across four LLMs and mainstream benchmarks show that MineValiCoder significantly outperforms state-of-the-art methods. Specifically, it achieves Pass@1 scores of 96.34% on HumanEval, 87.40% on MBPP, 64.00% on APPS, and 51.33% on LiveCodeBench. These results demonstrate the effectiveness of MineValiCoder in mitigating LLM stochasticity and improving the reliability of automated code generation.

Related papers

• ZeroCoder: Can LLMs Improve Code Generation Without Ground-Truth Supervision? [13.984583399745157]
  Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm to improve it through execution-based feedback.<n>Existing work tried to use self-generated tests to ground rewards, but the lack of discriminative tests constrains the effect due to the sub-optimal performance of the model on test generation.<n>We present ZeroCoder, a fully label-free co-evolutionary framework that jointly trains a Coder and a Tester using execution feedback from self-generated code-test interactions.
  arXiv  Detail & Related papers  (2026-04-09T06:24:54Z)
• Code-A1: Adversarial Evolving of Code LLM and Test LLM via Reinforcement Learning [54.95476453942411]
  Reinforcement learning for code generation relies on verifiable rewards from unit test pass rates.<n>Recent self-play methods unify code and test generation in a single model.<n>Code-A1 achieves code generation performance matching or exceeding models trained on human-annotated tests.
  arXiv  Detail & Related papers  (2026-03-16T17:58:13Z)
• Consistency Meets Verification: Enhancing Test Generation Quality in Large Language Models Without Ground-Truth Solutions [1.9196411948992402]
  We present ConVerTest, a novel two-stage pipeline for synthesizing reliable tests without requiring prior code implementations.<n>Experiments on BIGCODEBENCH and LESS BASIC PYTHON PROBLEMS benchmarks demonstrate that ConVerTest improves test validity, line coverage, and mutation scores by up to 39%, 28%, and 18% respectively.
  arXiv  Detail & Related papers  (2026-02-11T04:40:38Z)
• CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation [71.42965967582147]
  Existing approaches attempt to synthesize test cases using Large Language Models (LLMs)<n>We propose a $textbfFeedback-Bench Iterative Framework$ for comprehensive test case construction.<n>Our dataset achieves an average True Positive Rate (TPR) of $89.37%$ and True Negative Rate (TNR) of $90.89%$, significantly outperforming the CodeContests and CodeContests+ by margins of $4.32%$ and $9.37%$, respectively.
  arXiv  Detail & Related papers  (2026-01-20T07:32:44Z)
• Use Property-Based Testing to Bridge LLM Code Generation and Validation [38.25155484701058]
  Large Language Models (LLMs) excel at code generation, but ensuring their outputs to be functionally correct is a persistent challenge.<n>This paper introduces Property-Generated Solver, a novel framework that leverages Property-Based Testing (PBT) to validate high-level program properties.<n>Property-Generated Solver employs two collaborative LLM-based agents: a Generator dedicated to code generation and iterative refinement, and a Tester that manages the PBT life-cycle.
  arXiv  Detail & Related papers  (2025-06-23T06:01:12Z)
• KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding [49.56049319037421]
  KodCode is a synthetic dataset that addresses the persistent challenge of acquiring high-quality, verifiable training data.<n>It comprises question-solution-test triplets that are systematically validated via a self-verification procedure.<n>This pipeline yields a large-scale, robust and diverse coding dataset.
  arXiv  Detail & Related papers  (2025-03-04T19:17:36Z)
• Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation [69.62857948698436]
  Recent advances in large language models (LLMs) have improved their performance on coding benchmarks.<n>However, improvement is plateauing due to the exhaustion of readily available high-quality data.<n>We propose Sol-Ver, a self-play solver-verifier framework that jointly improves a single model's code and test generation capacity.
  arXiv  Detail & Related papers  (2025-02-20T18:32:19Z)
• Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy [0.5057850174013127]
  Modern Large Language Model (LLM)-based programming agents often rely on test execution feedback to refine their generated code.<n>This paper introduces VALTEST, a novel framework that leverages semantic entropy to automatically validate test cases generated by LLMs.<n>Experiments show that VALTEST boosts test validity by up to 29% and improves code generation performance, as evidenced by significant increases in pass@1 scores.
  arXiv  Detail & Related papers  (2024-11-13T00:07:32Z)
• CodeDPO: Aligning Code Models with Self Generated and Verified Source Code [52.70310361822519]
  We propose CodeDPO, a framework that integrates preference learning into code generation to improve two key code preference factors: code correctness and efficiency.<n>CodeDPO employs a novel dataset construction method, utilizing a self-generation-and-validation mechanism that simultaneously generates and evaluates code and test cases.
  arXiv  Detail & Related papers  (2024-10-08T01:36:15Z)
• CodeT: Code Generation with Generated Tests [49.622590050797236]
  We explore the use of pre-trained language models to automatically generate test cases. CodeT executes the code solutions using the generated test cases, and then chooses the best solution. We evaluate CodeT on five different pre-trained models with both HumanEval and MBPP benchmarks.
  arXiv  Detail & Related papers  (2022-07-21T10:18:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.