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The paper introduces iSWE Agent, a novel automated issue resolver specifically designed for Java code repositories, addressing the gap in existing LLM-based solutions that primarily focus on Python. iSWE Agent employs two sub-agents for localization and editing, leveraging novel tools based on rule-based Java static analysis and transformation. Experiments on Multi-SWE-bench and SWE-PolyBench demonstrate that iSWE achieves state-of-the-art issue resolution rates for Java, highlighting the benefits of combining rule-based and model-based techniques.
Java codebases can now get state-of-the-art automated issue resolution thanks to iSWE Agent, which outperforms existing LLM agents by combining rule-based static analysis with LLMs.
Resolving issues on code repositories is an important part of software engineering. Various recent systems automatically resolve issues using large language models and agents, often with impressive performance. Unfortunately, most of these models and agents focus primarily on Python, and their performance on other programming languages is lower. In particular, a lot of enterprise software is written in Java, yet automated issue resolution for Java is under-explored. This paper introduces iSWE Agent, an automated issue resolver with an emphasis on Java. It consists of two sub-agents, one for localization and the other for editing. Both have access to novel tools based on rule-based Java static analysis and transformation. Using this approach, iSWE achieves state-of-the-art issue resolution rates across the Java splits of both Multi-SWE-bench and SWE-PolyBench. More generally, we hope that by combining the best of rule-based and model-based techniques, this paper contributes towards improving enterprise software development.
