CodeScout: Contextual Problem Statement Enhancement for Software Agents

• URL: http://arxiv.org/abs/2603.05744v1
• Date: Thu, 05 Mar 2026 23:10:09 GMT
• Title: CodeScout: Contextual Problem Statement Enhancement for Software Agents
• Abstract summary: Current AI-powered code assistance tools often struggle with poorly-defined problem statements.<n>We introduce CodeScout, a contextual query refinement approach.<n>CodeScout converts underspecified user requests into comprehensive, actionable problem statements.
• Score: 14.947634004976505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current AI-powered code assistance tools often struggle with poorly-defined problem statements that lack sufficient task context and requirements specification. Recent analysis of software engineering agents reveals that failures on such underspecified requests are highly correlated with longer trajectories involving either over-exploration or repeated attempts at applying the same fix without proper evolution or testing, leading to suboptimal outcomes across software development tasks. We introduce CodeScout, a contextual query refinement approach that systematically converts underspecified user requests into comprehensive, actionable problem statements through lightweight pre-exploration of the target codebase. Our key innovation is demonstrating that structured analysis before task execution can supplement existing agentic capabilities without requiring any modifications to their underlying scaffolds. CodeScout performs targeted context scoping, conducts multi-perspective analysis examining potential fixes and exploration opportunities, then synthesizes these insights into enhanced problem statements with reproduction steps, expected behaviors, and targeted exploration hints. This pre-exploration directly addresses the identified failure patterns by reducing non-converging agent trajectories while clarifying user intent in natural language space. We evaluate CodeScout using state-of-the-art agentic scaffolds and language models on SWEBench-Verified, demonstrating a 20\% improvement in resolution rates with up to 27 additional issues resolved compared to the default baseline method. Our results suggest that systematic query refinement through contextual analysis represents a promising direction for enhancing AI code assistance capabilities.

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