mlsanityData to 7 Oct 2026

A3Test: Assertion-Augmented Automated Test Case Generation

arXiv 2302.10352 ↗First posted 20 Feb 2023cs.SEcited by 33

Not on the map. Why?

Abstract (arXiv)

Test case generation is an important activity, yet a time-consuming and laborious task. Recently, AthenaTest -- a deep learning approach for generating unit test cases -- is proposed. However, AthenaTest can generate less than one-fifth of the test cases correctly, due to a lack of assertion knowledge and test signature verification. In this paper, we propose A3Test, a DL-based test case generation approach that is augmented by assertion knowledge with a mechanism to verify naming consistency and test signatures. A3Test leverages the domain adaptation principles where the goal is to adapt the existing knowledge from an assertion generation task to the test case generation task. We also introduce a verification approach to verify naming consistency and test signatures. Through an evaluation of 5,278 focal methods from the Defects4j dataset, we find that our A3Test (1) achieves 147% more correct test cases and 15% more method coverage, with a lower number of generated test cases than AthenaTest; (2) still outperforms the existing pre-trained models for the test case generation task; (3) contributes substantially to performance improvement via our own proposed assertion pre-training and the verification components; (4) is 97.2% much faster while being more accurate than AthenaTest.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2023.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 8; 2025: 10; 2026: 10.

33 papers.

Sort:

By area:

  1. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 380
  2. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  3. 2025Co-Evolving LLM Coder and Unit Tester via Reinforcement Learningcited by 51
  4. 2024Human-In-the-Loop Software Development Agentscited by 42
  5. 2024CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarkscited by 23
  6. 2023Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generationcited by 21
  7. 2026SWE-Universe: Scale Real-World Verifiable Environments to Millionscited by 16
  8. 2025Learning to Generate Unit Test via Adversarial Reinforcement Learningcited by 11
  9. 2025Hallucination to Consensus: Multi-Agent LLMs for End-to-End JUnit Test Generationcited by 8
  10. 2024Retrieval-Augmented Test Generation: How Far Are We?cited by 7
  11. 2026Test vs Mutant: Adversarial LLM Agents for Robust Unit Test Generationcited by 6
  12. 2024UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testingcited by 5
21 more
  1. 2025Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassiancited by 4
  2. 2025Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesiscited by 4
  3. 2023Prompting Code Interpreter to Write Better Unit Tests on Quixbugs Functionscited by 3
  4. 2024Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Studycited by 3
  5. 2024Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropycited by 3
  6. 2024ViUniT: Visual Unit Tests for More Robust Visual Programmingcited by 3
  7. 2025Clarifying Semantics of In-Context Examples for Unit Test Generationcited by 3
  8. 2025EvoGPT: Leveraging LLM-Driven Seed Diversity to Improve Search-Based Test Suite Generationcited by 2
  9. 2025STAF: Leveraging LLMs for Automated Attack Tree-Based Security Test Generationcited by 2
  10. 2025Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Modelscited by 1
  11. 2025LLMs for Automated Unit Test Generation and Assessment in Java: The AgoneTest Frameworkcited by 1
  12. 2023Breaking the Silence: the Threats of Using LLMs in Software Engineeringcited by 0
  13. 2024Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generationcited by 0
  14. 2026An empirical study of LoRA-based fine-tuning of large language models for automated test case generationcited by 0
  15. 2026PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generationcited by 0
  16. 2026FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedbackcited by 0
  17. 2026Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)cited by 0
  18. 2026Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflowscited by 0
  19. 2026Fail-Aware and Explainable Test Oracle Predictioncited by 0
  20. 2026Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study)cited by 0
  21. 2026Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Testscited by 0