DOTA: Intelligent Debugging with Delta of Thoughts Agents

Abstract

Large language models (LLMs) have shown exceptional capabilities in code understanding and generation. However, they still face significant challenges in analyzing and debugging code. Most existing works rely on a single model, which often struggles to detect and fix bugs in complex program structures and semantic logic. This paper presents DoTA, a novel debugging framework that enhances LLMs' debugging capabilities through multiagent collaboration. Our key innovation is two-fold. First, we enhance code understanding through automated hierarchical documentation analysis, enabling more effective bug detection and localization based on comprehensive program context. Second, we leverage a delta-of-thoughts process where multi LLM agents analyze different aspects of program correctness and iteratively contribute complementary insights to identify bugs. Our experiments show that combining different LLM based agents with enriched documentation context significantly improves LLMs' debugging capabilities. DoTA has been extensively evaluated on the Debug- Bench dataset of 4,253 debugging instances. It achieves an average improvement of 15% in bug detection accuracy across languages compared to GPT-3.5 with task background prompting. The framework shows particular strength in handling complex logical errors (+18.3%) and multiple bugs (+18.9%). On open-source models, DoTA enhances bug detection capabilities by 10.4–13.5%.