# Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models arXiv: https://arxiv.org/abs/2502.16071 ยท HTML: https://mlsanity.com/paper/2502.16071/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in RAG for code, one of 15 directions in Retrieval-augmented generation. - Builds on 40 of its 68 references that are in the map. - First posted on arXiv 22 Feb 2025. ## Placed in - Retrieval-augmented generation / [RAG for code](https://mlsanity.com/rag/rag-for-code/) ## Builds on (most cited first, 30 of 40) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020) - [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020) - [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021) - [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021) - [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020) - [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022) - [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023) - [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023) - [Retrieval Augmented Code Generation and Summarization](https://mlsanity.com/paper/2108.11601/) (2021) - [Automating Code Review Activities by Large-Scale Pre-training](https://mlsanity.com/paper/2203.09095/) (2022) - [ChatUniTest: A Framework for LLM-Based Test Generation](https://mlsanity.com/paper/2305.04764/) (2023) - [DebugBench: Evaluating Debugging Capability of Large Language Models](https://mlsanity.com/paper/2401.04621/) (2024) - [Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks](https://mlsanity.com/paper/2102.02017/) (2021) - [Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM](https://mlsanity.com/paper/2402.00097/) (2024) - [Using Pre-Trained Models to Boost Code Review Automation](https://mlsanity.com/paper/2201.06850/) (2022) - [A Survey on Large Language Models for Software Engineering](https://mlsanity.com/paper/2312.15223/) (2023) - [TOGA: A Neural Method for Test Oracle Generation](https://mlsanity.com/paper/2109.09262/) (2021) - [A Survey of Learning-based Automated Program Repair](https://mlsanity.com/paper/2301.03270/) (2023) - [RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair](https://mlsanity.com/paper/2309.06057/) (2023) - [On Learning Meaningful Assert Statements for Unit Test Cases](https://mlsanity.com/paper/2002.05800/) (2020) - [A Survey on Deep Learning for Software Engineering](https://mlsanity.com/paper/2011.14597/) (2020) - [HITS: High-coverage LLM-based Unit Test Generation via Method Slicing](https://mlsanity.com/paper/2408.11324/) (2024) - [A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research](https://mlsanity.com/paper/2009.06520/) (2020) - [On the Evaluation of Large Language Models in Unit Test Generation](https://mlsanity.com/paper/2406.18181/) (2024) - [ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation](https://mlsanity.com/paper/2307.00588/) (2023) - [Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers](https://mlsanity.com/paper/2009.05634/) (2020) - [A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repair](https://mlsanity.com/paper/2310.08879/) (2023) ## Built on by (most cited first, 4 of 4) - [Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesis](https://mlsanity.com/paper/2510.26423/) (2025) - [MANGO: Automated Multi-Agent Test Oracle Generation for Vision-Language-Action Models](https://mlsanity.com/paper/2606.24815/) (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) Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual efforts in writing unit tests, some techniques have been proposed to automatically generate test assertions, with recent integration-based approaches considered state-of-the-art. Despite being promising, such integration-based approaches face several limitations, including reliance on lexical matching for assertion retrieval and a limited training corpus for assertion generation. This paper proposes a novel retrieval-augmented deep assertion generation approach, namely RetriGen, based on a hybrid retriever and a pre-trained language model (PLM)-based generator. Given a focal-test, RetriGen first builds a hybrid assertion retriever to search for the most relevant Test-Assert Pair from external codebases. The retrieval process considers lexical similarity and semantical similarity via a token-based and an embedding-based retriever, respectively. RetriGen then treats assertion generation as a sequence-to-sequence task and designs a PLM-based assertion generator to predict a correct assertion. We conduct extensive experiments to evaluate RetriGen against six state-of-the-art approaches across two large-scale datasets and two metrics. The results demonstrate that RetriGen achieves 57.66% accuracy and 73.24% CodeBLEU, outperforming all baselines with average improvements of 50.66% and 14.14%, respectively.