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LLM Test Generation via Iterative Hybrid Program Analysis

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arxiv 2503.13580 v2 pith:TNKEVX24 submitted 2025-03-17 cs.SE cs.AI

Sijia Gu , Noor Nashid , Ali Mesbah This is my paper
classification cs.SEcs.AI
keywords coveragetestgenerationanalysiscodeiterativepantabranch
verification ladder T0 review T1 audit T2 compute T3 formal
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Automating unit test generation remains a significant challenge, particularly for complex methods in real-world projects. While Large Language Models (LLMs) have made strides in code generation, they struggle to achieve high branch coverage due to their limited ability to reason about intricate control flow structures. To address this limitation, we introduce Panta, a technique that emulates the iterative process human developers follow when analyzing code and constructing test cases. Panta integrates static control flow analysis and dynamic code coverage analysis to systematically guide LLMs in identifying uncovered execution paths and generating better test cases. By incorporating an iterative feedback-driven mechanism, our technique continuously refines test generation based on static and dynamic path coverage insights, ensuring more comprehensive and effective testing. Our empirical evaluation, conducted on classes with high cyclomatic complexity from open-source projects, demonstrates that Panta achieves 26% higher line coverage and 23% higher branch coverage compared to the state-of-the-art.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Knowledge-Guided Synthetic Bug Feedback for LLM-Based Unit Test Generation

    cs.SE 2026-07 conditional novelty 6.5 of 10

    Mechanism-guided synthetic-bug feedback raises real-bug detection of LLM unit tests to 72.67% RBDR on 172 Defects4J tasks, outperforming mutation and coverage baselines by ~20 points.

  2. TATG: Tracking-Aware Testing Objective for LLM-based Test Generation

    cs.SE 2026-07 conditional novelty 6.0 of 10

    Tracking individual static and feedback testing objectives across a coverage-then-mutation two-stage LLM workflow raises line/branch coverage and mutation score on complex Java methods versus KTester and PANTA.

  3. Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming

    cs.SE 2026-07 unverdicted novelty 6.0 of 10

    VLP adds an NL documentation layer with trace-linked mismatch detection and derived formal checks to make human validation of LLM code feasible, lifting pass@1 from 28.7-73.2% to 65.4-93.5%.

  4. LLM-based Mockless Unit Test Generation for Java

    cs.SE 2026-05 unverdicted novelty 6.0 of 10

    MocklessTester generates mockless Java unit tests using context mining and multi-level constraint fixing, improving coverage and mutation scores over baselines on Defects4J and Deps4J.

  5. Call-Chain-Aware LLM-Based Test Generation for Java Projects

    cs.SE 2026-04 unverdicted novelty 6.0 of 10

    CAT improves line coverage by 18% and branch coverage by 22% over prior LLM test generation methods by adding call-chain and dependency context from static analysis to prompts.

  6. TestDecision: Sequential Test Suite Generation via Greedy Optimization and Reinforcement Learning

    cs.SE 2026-04 unverdicted novelty 6.0 of 10

    By proving test suite coverage is monotone submodular and training LLMs with RL to maximize marginal gains, TestDecision improves branch coverage 38-52% and bug detection up to 95% over base models on ULT and LiveCodeBench.

  7. Hamster: A Large-Scale Study and Characterization of Developer-Written Tests

    cs.SE 2025-09 conditional novelty 6.0 of 10

    A large-scale study of 1.7M developer-written Java tests shows they commonly use fixtures, mocking, structured inputs, and multi-class scope, features largely missing from automated test generation tools.

  8. Mutation-Guided Unit Test Generation with a Large Language Model

    cs.SE 2025-06 conditional novelty 6.0 of 10

    MUTGEN incorporates mutation feedback into LLM prompts and uses iteration to generate unit tests that achieve higher mutation scores than EvoSuite or vanilla LLM prompting on 204 benchmark subjects.

  9. How well LLM-based test generation techniques perform with newer LLM versions?

    cs.SE 2026-01 conditional novelty 5.0 of 10

    With newer LLMs, a plainly prompted generation loop matches or beats four engineered test-generation tools on coverage and mutation score, and a class-then-method hybrid cuts LLM queries by about 20%.

  10. ATLAS: Multi-View Code Representation Tool for C and C++ Source Programs

    cs.SE 2025-12 conditional novelty 5.0 of 10

    ATLAS claims no-build aligned AST/CFG/DFG extraction for C/C++ at 96.8%/91.7% CFG correctness, and a 34.7-point LLM line-coverage lift when fed its CFG paths.

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