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AutoJML: Generation and Verification of JML Specifications Using LLM Agents
IEEE Pulse
|May 15, 2026
Summary
AutoJML, an AI agent, automatically generates and refines Java Modeling Language (JML) specifications. It improves program verification, especially for complex code with multiple paths and loops.
Area of Science:
- Software Engineering
- Formal Methods
- Artificial Intelligence
Background:
- Formal specifications like Java Modeling Language (JML) are crucial for program verification.
- Manual creation of JML specifications is complex and prone to errors.
- Existing automated methods using large language models (LLMs) face challenges with intricate control flow and semantic completeness.
Purpose of the Study:
- To introduce AutoJML, an LLM-based agent designed for automated generation and refinement of JML specifications.
- To enhance the accuracy and completeness of JML specifications for Java programs.
- To address the limitations of current LLM approaches in handling complex program structures.
Main Methods:
- Development of an LLM agent, AutoJML, employing iterative verification.
- Integration of mutation-based feedback for refining generated specifications.
- Evaluation on a diverse set of Java programs featuring varied control flow patterns.
Main Results:
- AutoJML demonstrated superior performance in verifying programs compared to a state-of-the-art baseline.
- The agent showed particular effectiveness in handling programs with multipath control flow and nested loops.
- Successful generation and refinement of semantically complete JML specifications for complex Java code.
Conclusions:
- AutoJML offers a robust solution for automating the creation of JML specifications.
- The iterative verification and feedback mechanism significantly improves specification quality.
- AutoJML advances the field of automated program verification for complex software systems.
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