# AsserT5: Test Assertion Generation Using a Fine-Tuned Code Language Model

arXiv: https://arxiv.org/abs/2502.02708 · HTML: https://mlsanity.com/paper/2502.02708/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2025.

## Built on by (most cited first, 7 of 7)

- [Beyond Postconditions: Can Large Language Models infer Formal Contracts for Automatic Software Verification?](https://mlsanity.com/paper/2510.12702/) (2025)
- [Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey](https://mlsanity.com/paper/2506.14640/) (2025)
- [Large Language Models for Software Testing: A Research Roadmap](https://mlsanity.com/paper/2509.25043/) (2025)
- [ContractEval: A Benchmark for Evaluating Contract-Satisfying Assertions in Code Generation](https://mlsanity.com/paper/2510.12047/) (2025)
- [All Smoke, No Alarm: Oracle Signals in Agent-Authored Test Code](https://mlsanity.com/paper/2606.18168/) (2026)
- [Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)](https://mlsanity.com/paper/2607.05032/) (2026)
- [Agent-Based Test Assertion Generation via Diverse Perspective Aggregation](https://mlsanity.com/paper/2608.05822/) (2026)

## Abstract (arXiv)

Writing good software tests can be challenging, therefore approaches that support developers are desirable. While generating complete tests automatically is such an approach commonly proposed in research, developers may already have specific test scenarios in mind and thus just require help in selecting the most suitable test assertions for these scenarios. This can be done using deep learning models to predict assertions for given test code. Prior research on assertion generation trained these models specifically for the task, raising the question how much the use of larger models pre-trained on code that have emerged since then can improve their performance. In particular, while abstracting identifiers has been shown to improve specifically trained models, it remains unclear whether this also generalises to models pre-trained on non-abstracted code. Finally, even though prior work demonstrated high accuracy it remains unclear how this translates into the effectiveness of the assertions at their intended application -- finding faults. To shed light on these open questions, in this paper we propose AsserT5, a new model based on the pre-trained CodeT5 model, and use this to empirically study assertion generation. We find that the abstraction and the inclusion of the focal method are useful also for a fine-tuned pre-trained model, resulting in test assertions that match the ground truth assertions precisely in up to 59.5% of cases, more than twice as precise as prior models. However, evaluation on real bugs from the Defects4J dataset shows that out of 138 bugs detectable with assertions in real-world projects, AsserT5 was only able to suggest fault-finding assertions for 33, indicating the need for further improvements.