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Exploring Automated Assertion Generation via Large Language Models

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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MeMo: Automatically identifying metamorphic relations in Javadoc comments for test automation
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Gamma: Revisiting Template-Based Automated Program Repair Via Mask Prediction
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Revisiting and Improving Retrieval-Augmented Deep Assertion Generation
inferredcites →https://doi.org/10.1109/icse-seip.2017.27openalex-snapshot
An Industrial Evaluation of Unit Test Generation: Finding Real Faults in a Financial Application
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Feedback-Directed Random Test Generation
inferredcites →https://doi.org/10.1109/icse.2012.6227137openalex-snapshot
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Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks
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Automated Program Repair in the Era of Large Pre-trained Language Models
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An Empirical Study of Deep Learning Models for Vulnerability Detection
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Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning
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A study of integration testing and software regression at the integration level
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@tComment: Testing Javadoc Comments to Detect Comment-Code Inconsistencies
inferredcites →https://doi.org/10.1109/issre.2014.11openalex-snapshot
A Survey on Unit Testing Practices and Problems
inferredcites →https://doi.org/10.1109/tse.2012.14openalex-snapshot
Whole Test Suite Generation
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An Empirical Study on the Usage of Transformer Models for Code Completion
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Using Transfer Learning for Code-Related Tasks
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An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation
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Defects4J: a database of existing faults to enable controlled testing studies for Java programs
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The plastic surgery hypothesis
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An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
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C2S: translating natural language comments to formal program specifications
inferredcites →https://doi.org/10.1145/3377811.3380429openalex-snapshot
On learning meaningful assert statements for unit test cases
inferredcites →https://doi.org/10.1145/3510003.3510141openalex-snapshot
TOGA
inferredcites →https://doi.org/10.1145/3510003.3510149openalex-snapshot
Automated assertion generation via information retrieval and its integration with deep learning
inferredcites →https://doi.org/10.1145/3510003.3510621openalex-snapshot
Using pre-trained models to boost code review automation
inferredcites →https://doi.org/10.1145/3524481.3527220openalex-snapshot
Generating accurate assert statements for unit test cases using pretrained transformers
inferredcites →https://doi.org/10.1145/3524842.3528452openalex-snapshot
LineVul
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CIRCLE: continual repair across programming languages
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Automating code review activities by large-scale pre-training
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VulRepair: a T5-based automated software vulnerability repair
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Towards More Realistic Evaluation for Neural Test Oracle Generation
inferredcites →https://doi.org/10.1145/3597926.3598135openalex-snapshot
How Effective Are Neural Networks for Fixing Security Vulnerabilities
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A Survey of Learning-based Automated Program Repair
inferredcites →https://doi.org/10.1145/3632742openalex-snapshot
An Extractive-and-Abstractive Framework for Source Code Summarization
inferredcites →https://doi.org/10.1145/3663529.3663801openalex-snapshot
ChatUniTest: A Framework for LLM-Based Test Generation
inferredcites →https://doi.org/10.1145/566172.566207openalex-snapshot
Is ISSTA research relevant to industry?
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.139openalex-snapshot
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.685openalex-snapshot
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
inferredcites →https://doi.org/10.18653/v1/2021.naacl-main.211openalex-snapshot
Unified Pre-training for Program Understanding and Generation
inferredcites →https://doi.org/10.18653/v1/2022.acl-long.499openalex-snapshot
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
inferredcites →https://doi.org/10.18653/v1/2023.emnlp-main.68openalex-snapshot
CodeT5+: Open Code Large Language Models for Code Understanding and Generation
inferredcites →https://doi.org/10.18653/v1/d13-1176openalex-snapshot
Recurrent Continuous Translation Models
inferredcites →https://doi.org/10.18653/v1/n19-1423openalex-snapshot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
inferredcites →https://doi.org/10.3115/1613715openalex-snapshot
Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
inferredcites →https://doi.org/10.48550/arxiv.2203.13474openalex-snapshot
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
inferredcites →https://doi.org/10.48550/arxiv.2305.04207openalex-snapshot
No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation
inferredcites →https://doi.org/10.48550/arxiv.2305.06161openalex-snapshot
StarCoder: may the source be with you!
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A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repair

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Automated test plan generation using large language models
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LLM-Based Test Oracles: Source-of-Authority Taxonomy—A Systematic Literature Review
inferred← citeshttps://doi.org/10.1109/aiware69974.2025.00011openalex-snapshot
Understanding LLM-Driven Test Oracle Generation
inferred← citeshttps://doi.org/10.1109/isvlsi65124.2025.11130226openalex-snapshot
LISA: LLM Informed Systemverilog Assertion generation with RAG and Chain-of-Thought
inferred← citeshttps://doi.org/10.1109/satc69565.2026.11542349openalex-snapshot
Knowledge Graph-Driven Automated Generation of SystemVerilog Assertions from Design Specifications
inferred← citeshttps://doi.org/10.1109/tifs.2026.3723190openalex-snapshot
Facilitating Logical Flaw Detection for Autonomous Driving Systems Through LLM-Empowered Oracle Generation
inferred← citeshttps://doi.org/10.1109/tse.2025.3545970openalex-snapshot
Improving Retrieval-Augmented Deep Assertion Generation via Joint Training
inferred← citeshttps://doi.org/10.1109/tse.2025.3558403openalex-snapshot
Retrieval-Augmented Fine-Tuning for Improving Retrieve-and-Edit Based Assertion Generation
inferred← citeshttps://doi.org/10.1109/vts65138.2025.11022932openalex-snapshot
Special Session: ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification
inferred← citeshttps://doi.org/10.1145/3715003openalex-snapshot
The Future of AI-Driven Software Engineering
inferred← citeshttps://doi.org/10.1145/3715107openalex-snapshot
Test Oracle Automation in the Era of LLMs
inferred← citeshttps://doi.org/10.1145/3721128openalex-snapshot
Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-Trained Language Models
inferred← citeshttps://doi.org/10.1145/3728951openalex-snapshot
A Large-Scale Empirical Study on Fine-Tuning Large Language Models for Unit Testing
inferred← citeshttps://doi.org/10.1145/3799693openalex-snapshot
A Systematic Literature Review on Large Language Models for Automated Program Repair
inferred← citeshttps://doi.org/10.1145/3802827openalex-snapshot
Enhancing Automated Unit Test Generation with Large Language Models: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1145/3803418openalex-snapshot
Hallucination to Consensus: Multi-Agent LLMs for End-to-End JUnit Test Generation
inferred← citeshttps://doi.org/10.1145/3819237openalex-snapshot
Fusing LLMs and Genetic Algorithm for High-Quality Unit Test Generation
inferred← citeshttps://doi.org/10.1145/3830083openalex-snapshot
Type-aware LLM-based Test Generation for Python Programs
inferred← citeshttps://doi.org/10.1145/3832214openalex-snapshot
Towards More Realistic Assertion Generation under Mixed-Assertion Scenario
inferred← citeshttps://doi.org/10.1145/3832283openalex-snapshot
STARS: Static Analysis-Guided Assertion Synthesis using Large Language Models
inferred← citeshttps://doi.org/10.1145/3832783.3837478openalex-snapshot
FAiLGen: Calibrating LLM for Failing Test Generation with Monte Carlo Tree Search
inferred← citeshttps://doi.org/10.1145/3843770openalex-snapshot
BASFuzz: Toward Robustness Evaluation of LLM-based NLP Software via Automated Fuzz Testing
inferred← citeshttps://doi.org/10.5753/sbqs.2025.14558openalex-snapshot
Exploring ChatGPT Efficiency in Automatic Test Generation for Python: A Comparative Analysis
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