mlsanityData to 7 Oct 2026

Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesis

arXiv 2510.26423 ↗First posted 30 Oct 2025cs.SE cs.CLcited by 4

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Test oracle generation in non-regression testing is a longstanding challenge in software engineering, where the goal is to produce oracles that can accurately determine whether a function under test (FUT) behaves as intended for a given input. In this paper, we introduce Nexus, a novel multi-agent framework to address this challenge. Nexus generates test oracles by leveraging a diverse set of specialized agents that synthesize test oracles through a structured process of deliberation, validation, and iterative self-refinement. During the deliberation phase, a panel of four specialist agents, each embodying a distinct testing philosophy, collaboratively critiques and refines an initial set of test oracles. Then, in the validation phase, Nexus generates a plausible candidate implementation of the FUT and executes the proposed oracles against it in a secure sandbox. For any oracle that fails this execution-based check, Nexus activates an automated selfrefinement loop, using the specific runtime error to debug and correct the oracle before re-validation. Our extensive evaluation on seven diverse benchmarks demonstrates that Nexus consistently and substantially outperforms state-of-theart baselines. For instance, Nexus improves the test-level oracle accuracy on the LiveCodeBench from 46.30% to 57.73% for GPT-4.1-Mini. The improved accuracy also significantly enhances downstream tasks: the bug detection rate of GPT4.1-Mini generated test oracles on HumanEval increases from 90.91% to 95.45% for Nexus compared to baselines, and the success rate of automated program repair improves from 35.23% to 69.32%.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 31 of its 52 references that are in the map. First posted on arXiv 30 Oct 2025.

Builds on

31 of 52 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Hallucination to Consensus: Multi-Agent LLMs for End-to-End JUnit Test Generation3 Jun

Other references · 30

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2021Program Synthesis with Large Language Modelscited by 3,509
  3. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  4. 2023An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generationcited by 193
  5. 2020Unit Test Case Generation with Transformers and Focal Contextcited by 118
  6. 2024Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLMcited by 63
  7. 2024EffiBench: Benchmarking the Efficiency of Automatically Generated Codecited by 62
  8. 2021TOGA: A Neural Method for Test Oracle Generationcited by 57
  9. 2023Using Large Language Models to Generate JUnit Tests: An Empirical Studycited by 53
  10. 2023Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?cited by 53
  11. 2023Effective Test Generation Using Pre-trained Large Language Models and Mutation Testingcited by 51
  12. 2024HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generationcited by 39
18 more
  1. 2024TESTEVAL: Benchmarking Large Language Models for Test Case Generationcited by 38
  2. 2024HITS: High-coverage LLM-based Unit Test Generation via Method Slicingcited by 37
  3. 2024CoverUp: Effective High Coverage Test Generation for Pythoncited by 36
  4. 2023A3Test: Assertion-Augmented Automated Test Case Generationcited by 33
  5. 2023CAT-LM: Training Language Models on Aligned Code And Testscited by 28
  6. 2025EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Codecited by 24
  7. 2024TOGLL: Correct and Strong Test Oracle Generation with LLMscited by 23
  8. 2023Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generationcited by 21
  9. 2024TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iterationcited by 20
  10. 2024Advancing Code Coverage: Incorporating Program Analysis with Large Language Modelscited by 8
  11. 2025Benchmarking LLMs for Unit Test Generation from Real-World Functionscited by 8
  12. 2023Domain Adaptation for Code Model-based Unit Test Case Generationcited by 7
  13. 2024LLM-based Unit Test Generation via Property Retrievalcited by 6
  14. 2025Large Language Models for Unit Testing: A Systematic Literature Reviewcited by 6
  15. 2024UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testingcited by 5
  16. 2024CasModaTest: A Cascaded and Model-agnostic Self-directed Framework for Unit Test Generationcited by 4
  17. 2025Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Modelscited by 4
  18. 2024FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026The Rise of Agentic Testing: Multi-Agent Systems for Robust Software Quality Assurance
  2. 2026VibeCheck: Assessing the Quality of LLM-Generated Unit Tests: A Multi-agent Empirical Study across Heterogeneous Repositories
  3. 2025TestForge: Feedback-Driven, Agentic Test Suite Generation
  4. 2026Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skills
  5. 2026Complex Agents, Shallow Tests: Demystifying and Enhancing Test Adequacy of Agent Harness in the Wild
  6. 2026AgentForge: Execution-Grounded Multi-Agent LLM Framework for Autonomous Software Engineering
  7. 2026Grounding AI Agents in Contracts: An Empirical Evaluation of Spec-Driven Test Generation
  8. 2026All Smoke, No Alarm: Oracle Signals in Agent-Authored Test Code