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[2601.12522] Improved Bug Localization with AI Agents Leveraging Hypothesis and Dynamic Cognition
Summary
The paper presents CogniGent, a novel AI technique for bug localization that enhances traditional methods by leveraging causal reasoning and dynamic cognitive debugging, significantly improving performance metrics over existing techniques.
Why It Matters
Software bugs are a major cost for technology providers, consuming significant developer time. This research introduces a new approach that combines AI with human-like reasoning to improve bug localization, potentially transforming software development practices and reducing costs.
Key Takeaways
- CogniGent utilizes multiple AI agents for enhanced bug localization.
- The technique incorporates causal reasoning and context engineering.
- Experimental results show significant performance improvements over traditional methods.
- CogniGent emulates human debugging practices for better results.
- Statistical tests confirm the effectiveness of the proposed method.
Computer Science > Software Engineering arXiv:2601.12522 (cs) [Submitted on 18 Jan 2026 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Improved Bug Localization with AI Agents Leveraging Hypothesis and Dynamic Cognition Authors:Asif Mohammed Samir, Mohammad Masudur Rahman View a PDF of the paper titled Improved Bug Localization with AI Agents Leveraging Hypothesis and Dynamic Cognition, by Asif Mohammed Samir and 1 other authors View PDF HTML (experimental) Abstract:Software bugs cost technology providers (e.g., AT&T) billions annually and cause developers to spend roughly 50% of their time on bug resolution. Traditional methods for bug localization often analyze the suspiciousness of code components (e.g., methods, documents) in isolation, overlooking their connections with other components in the codebase. Recent advances in Large Language Models (LLMs) and agentic AI techniques have shown strong potential for code understanding, but still lack causal reasoning during code exploration and struggle to manage growing context effectively, limiting their capability. In this paper, we present a novel agentic technique for bug localization -- CogniGent -- that overcomes the limitations above by leveraging multiple AI agents capable of causal reasoning, call-graph-based root cause analysis and context engineering. It emulates developers-inspired debugging practices (a.k.a., dynamic cognitive debugging) and conducts hypothesis testing to support bug localization. We ev...
