Doc2OracLL: Investigating the Impact of Documentation on LLM-based Test Oracle Generation
Not on the map. Why?
Abstract (arXiv)
Code documentation is a critical aspect of software development, serving as a bridge between human understanding and machine-readable code. Beyond assisting developers in understanding and maintaining code, documentation also plays a critical role in automating various software engineering tasks, such as test oracle generation (TOG). In Java, Javadoc comments provide structured, natural language documentation embedded directly in the source code, typically detailing functionality, usage, parameters, return values, and exceptions. While prior research has utilized Javadoc comments in test oracle generation (TOG), there has not been a thorough investigation into their impact when combined with other contextual information, nor into identifying the most relevant components for generating correct and strong test oracles, or understanding their role in detecting real bugs. In this study, we dive deep into investigating the impact of Javadoc comments on TOG.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Dec 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 2; 2026: 8.
10 papers.
- 2025Seeing is Fixing: Cross-Modal Reasoning with Multimodal LLMs for Visual Software Issue Fixingcited by 11
- 2025Beyond Postconditions: Can Large Language Models infer Formal Contracts for Automatic Software Verification?cited by 5
- 2026Understanding LLM-Driven Test Oracle Generationcited by 2
- 2026LogicHunter: Testing LLM Agent Frameworks with an Agentic Oraclecited by 1
- 2026Multi-Agent Coordinated Rename Refactoringcited by 0
- 2026FLARE: Agentic Coverage-Guided Fuzzing for LLM-Based Multi-Agent Systemscited by 0
- 2026Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)cited by 0
- 2026Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphscited by 0
- 2026Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study)cited by 0
- 2026Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Testscited by 0
- 2026Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests24 Jul 2026
- 2026Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study)24 Jul 2026
- 2026Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs14 Jul 2026
- 2026LogicHunter: Testing LLM Agent Frameworks with an Agentic Oracle7 Jul 2026
- 2026Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)6 Jul 2026
- 2026FLARE: Agentic Coverage-Guided Fuzzing for LLM-Based Multi-Agent Systems7 Apr 2026
- 2026Understanding LLM-Driven Test Oracle Generation9 Jan 2026
- 2026Multi-Agent Coordinated Rename Refactoring1 Jan 2026
- 2025Beyond Postconditions: Can Large Language Models infer Formal Contracts for Automatic Software Verification?14 Oct 2025
- 2025Seeing is Fixing: Cross-Modal Reasoning with Multimodal LLMs for Visual Software Issue Fixing19 Jun 2025