# Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers

arXiv: https://arxiv.org/abs/2009.05634 · HTML: https://mlsanity.com/paper/2009.05634/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

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

- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [Planning with Large Language Models for Code Generation](https://mlsanity.com/paper/2303.05510/) (2023)
- [Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?](https://mlsanity.com/paper/2310.01831/) (2023)
- [Interactive Code Generation via Test-Driven User-Intent Formalization](https://mlsanity.com/paper/2208.05950/) (2022)
- [What do pre-trained code models know about code?](https://mlsanity.com/paper/2108.11308/) (2021)
- [Training and Evaluating a Jupyter Notebook Data Science Assistant](https://mlsanity.com/paper/2201.12901/) (2022)
- [Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy](https://mlsanity.com/paper/2109.08780/) (2021)
- [B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests](https://mlsanity.com/paper/2409.08692/) (2024)
- [Domain Adaptation for Code Model-based Unit Test Case Generation](https://mlsanity.com/paper/2308.08033/) (2023)
- [Assessing Evaluation Metrics for Neural Test Oracle Generation](https://mlsanity.com/paper/2310.07856/) (2023)
- [On The Cross-Modal Transfer from Natural Language to Code through Adapter Modules](https://mlsanity.com/paper/2204.08653/) (2022)
- [Perfect is the enemy of test oracle](https://mlsanity.com/paper/2302.01488/) (2023)
- [Beyond Postconditions: Can Large Language Models infer Formal Contracts for Automatic Software Verification?](https://mlsanity.com/paper/2510.12702/) (2025)
- [Revisiting and Improving Retrieval-Augmented Deep Assertion Generation](https://mlsanity.com/paper/2309.10264/) (2023)
- [Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap](https://mlsanity.com/paper/2512.04680/) (2025)
- [exLong: Generating Exceptional Behavior Tests with Large Language Models](https://mlsanity.com/paper/2405.14619/) (2024)
- [CodeSift: An LLM-Based Reference-Less Framework for Automatic Code Validation](https://mlsanity.com/paper/2408.15630/) (2024)
- [Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models](https://mlsanity.com/paper/2502.16071/) (2025)
- [AdaptivePaste: Code Adaptation through Learning Semantics-aware Variable Usage Representations](https://mlsanity.com/paper/2205.11023/) (2022)
- [LLMs in the Heart of Differential Testing: A Case Study on a Medical Rule Engine](https://mlsanity.com/paper/2404.03664/) (2024)
- [Improving Retrieval-Augmented Deep Assertion Generation via Joint Training](https://mlsanity.com/paper/2502.10696/) (2025)
- [Large Language Models for Software Testing: A Research Roadmap](https://mlsanity.com/paper/2509.25043/) (2025)
- [Understanding LLM-Driven Test Oracle Generation](https://mlsanity.com/paper/2601.05542/) (2026)
- [SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference](https://mlsanity.com/paper/2602.20610/) (2026)
- [Static Program Analysis Guided LLM Based Unit Test Generation](https://mlsanity.com/paper/2503.05394/) (2025)
- [Teaching Code LLMs to Reason with Intermediate Formal Specifications](https://mlsanity.com/paper/2607.04232/) (2026)
- [SCATE: Learning to Supervise Coding Agents for Cost-Effective Test Generation](https://mlsanity.com/paper/2607.08983/) (2026)
- [Embedding Code Contexts for Cryptographic API Suggestion:New Methodologies and Comparisons](https://mlsanity.com/paper/2103.08747/) (2021)
- [CUBETESTERAI: Automated JUnit Test Generation using the LLaMA Model](https://mlsanity.com/paper/2504.15286/) (2025)
- [Fail-Aware and Explainable Test Oracle Prediction](https://mlsanity.com/paper/2607.11342/) (2026)

## Abstract (arXiv)

Unit testing represents the foundational basis of the software testing pyramid, beneath integration and end-to-end testing. Automated software testing researchers have proposed a variety of techniques to assist developers in this time-consuming task. In this paper we present an approach to support developers in writing unit test cases by generating accurate and useful assert statements. Our approach is based on a state-of-the-art transformer model initially pretrained on an English textual corpus. This semantically rich model is then trained in a semi-supervised fashion on a large corpus of source code. Finally, we finetune this model on the task of generating assert statements for unit tests. The resulting model is able to generate accurate assert statements for a given method under test. In our empirical evaluation, the model was able to predict the exact assert statements written by developers in 62% of the cases in the first attempt. The results show 80% relative improvement for top-1 accuracy over the previous RNN-based approach in the literature. We also show the substantial impact of the pretraining process on the performances of our model, as well as comparing it with assert auto-completion task. Finally, we demonstrate how our approach can be used to augment EvoSuite test cases, with additional asserts leading to improved test coverage.