cs.SEFeb 20, 2026

Debug2Fix: Can Interactive Debugging Help Coding Agents Fix More Bugs?

Authors: Spandan Garg, Yufan Huang

Organizations: Microsoft USA

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Abstract

While significant progress has been made in automating various aspects of software development through coding agents, there is still significant room for improvement in their bug fixing capabilities. Debugging and investigation of runtime behavior remains largely a manual, developer-driven process. Popular coding agents typically rely on either static analysis of the code or iterative test-fix cycles, which is akin to trial and error debugging. We posit that there is a wealth of rich runtime information that developers routinely access while debugging code, which agents are currently deprived of due to design limitations. Despite how prevalent debuggers are in modern IDEs and command-line tools, they have surprisingly not made their way into coding agents. In this work, we introduce Debug2Fix, a novel framework that incorporates interactive debugging as a core component of a software engineering agent via a subagent architecture. We incorporate debuggers for Java and Python into our agent framework and evaluate against GitBug-Java and SWE-Bench-Live and achieve >20% improvement in performance compared to the baseline for certain models. Furthermore, using our framework, we're able to make weaker models like GPT-5 and Claude Haiku 4.5 match or exceed the performances of stronger models like Claude Sonnet 4.5, showing that better tool design is often just as important as switching to a more expensive model. Finally, we conduct systematic ablations demonstrating the importance of both the subagent architecture and debugger integration.

Figures & tables

Figure 1. A high-level view of the overall Debug2Fix pipeline with the Debug Subagent. We can see that the main agent’s is to loop between querying the Debug Subagent, followed by making fixes based on the learned insights from runtime behavior. Internally, the Debug Subagent works by going through a cycle of setting breakpoints, stepping through code and inspecting variables / expressions until it has the answer to main agent’s query or runs out of turns.
Figure 2. A bug from a popular open-source Python repository on GitHub. We see very different trajectories taken by the Baseline Agent and Debug2Fix. In the baseline, we see the agent doing repeated print-debug cycles and arriving at the wrong fix due to not being able to find the root cause of the issue, which is situated deep within the repo. With Debug2Fix, the agent uses the Debug Subagent which is able to find the root cause immediately using a debugger. This results in the agent arriving at the correct fix.
Table 1. Comparison of success rate for each configuration. We also show the breakdown of steps and tokens used by the main agent and the Debug Subagent within each of our configurations. For each number, we also show the relative improvement or decrease in performance over baseline.
Table 2. Comparing the performances, token usages and steps used by Debug2Fix and Baseline agent on the Python subset of SWE-Bench Live dataset.
CategoryDescription% Cases# Instances
Exception DiagnosisIdentifying the type, origin, or cause of runtime exceptions.27.8%17
Root Cause AnalysisUnderstanding why a specific behavior or bug occurs.25.3%15
Local Variable InspectionQuerying the value of specific variables at a location.15.2%11
Attribute Value InspectionInspecting object attributes or map entries.15.2%9
Assertion FailureIdentifying which assertion fails and the actual vs expected values.6.3%5
Code ReachabilityChecking if execution reaches a specific location or branch.5.1%4
Table 3. Categorization of the kinds of questions we observed the main agent posing to the Debug Subagent in a sample of 50 instances.
Failure ModeDescription% Cases# Instances
Debugger Session FailedDebug Subagent could not attach JDB due to failed build/test. This could be due to a variety of reasons like missing Gradle task, test process exited before debugger attached, etc.36%18
Wrong Fix Despite Correct DebuggingDebug Subagent successfully answered the runtime question, but the main agent applied an incorrect or incomplete fix.34%17
High Complexity BugBug required >> 3 debug sessions, yet the agent could not converge on the correct fix despite these multiple attempts.16%8
Subagent API ErrorDebug Subagent request failed due to server errors, which results in an empty response.8%4
Subagent Static AnalysisDebug Subagent ends up doing static code analysis, providing incomplete information without actual runtime values.6%3
Table 4. Failure modes observed in failed Debug2Fix instances despite having the Debug Subagent.
Figure 5. Plot showing an aggregated view of all the trajectories taken by the Debug Subagent. Each step shows a distribution of tools called within it. We can see the plot tapering to the right because more trajectories resolves as the subagent takes more steps.
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