# A3Test: Assertion-Augmented Automated Test Case Generation

arXiv: https://arxiv.org/abs/2302.10352 · HTML: https://mlsanity.com/paper/2302.10352/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2023.

## Built on by (most cited first, 30 of 33)

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- [Prompting Code Interpreter to Write Better Unit Tests on Quixbugs Functions](https://mlsanity.com/paper/2310.00483/) (2023)
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- [Breaking the Silence: the Threats of Using LLMs in Software Engineering](https://mlsanity.com/paper/2312.08055/) (2023)
- [Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation](https://mlsanity.com/paper/2412.14308/) (2024)
- [An empirical study of LoRA-based fine-tuning of large language models for automated test case generation](https://mlsanity.com/paper/2604.06946/) (2026)
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- [FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback](https://mlsanity.com/paper/2605.01264/) (2026)
- [Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)](https://mlsanity.com/paper/2607.05032/) (2026)
- [Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflows](https://mlsanity.com/paper/2607.09101/) (2026)

## 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.