CHAPTER 5
RESULTS AND DISCUSSION
This chapter presents the findings from the evaluation of Trace across three dimensions: debugging effectiveness, user experience and usability, and system architecture performance. The evaluation combined quantitative measurements from benchmark experiments and ablation studies with qualitative insights gathered through user study sessions. Following the presentation of results, I discuss the implications of these findings, compare Trace with existing tools, and acknowledge the limitations of this research.
5.1 DEBUGGING EFFECTIVENESS (RQ1, H1)
The primary goal of this evaluation was to determine whether Trace can effectively resolve real backend bugs. I evaluated Trace on a curated set of 10 reproducible bugs drawn from open-source Node.js and Express applications, comparing its performance against a one-shot LLM baseline where GPT-4 was provided with the bug description and relevant code but lacked iterative tool use or runtime verification.
5.1.1 Overall Success Rates
Table 5.1 summarizes the primary effectiveness results across all benchmark tasks.
Table 5.1: Overall Debugging Effectiveness Results
Metric Trace One-Shot LLM Baseline
Tasks Successfully Fixed 7/10 (70%) 3/10 (30%)
Tasks Partially Fixed 2/10 (20%) 2/10 (20%)
Tasks Failed 1/10 (10%) 5/10 (50%)
Median Iterations (successful) 4 1 (by definition)
Median Time to Fix (successful) 6.2 min N/A
Trace achieved a success rate of 70%, resolving 7 out of 10 benchmark bugs compared to only 3 out of 10 for the one-shot baseline. This represents a relative improvement of 133% in success rate. The difference was statistically significant using Fisher’s exact test (p = 0.037), providing support for H1.
Two tasks were classified as “partially fixed” for both approaches, meaning the generated patch addressed some but not all failing test cases. One task—involving a race condition in an asynchronous cache invalidation routine—proved too complex for both approaches within the iteration and time limits.
5.1.2 Task-Level Analysis
Table 5.2 provides a detailed breakdown of results by individual task, including bug characteristics and outcomes.
Table 5.2: Task-Level Debugging Results
Task Bug Type Files Involved Trace Result Iterations Time (min) Baseline Result
T1 Null reference in auth middleware ✓ Pass 3 4.1 ✗ Fail
T2 Incorrect date parsing in API response ✓ Pass 5 7.8 ✓ Pass
T3 Missing input validation on endpoint ✓ Pass 2 2.3 ✗ Fail
T4 Race condition in cache update ✗ Fail 10 (limit) 30.0 ✗ Fail
T5 Wrong HTTP status code returned ✓ Pass 1 1.2 ✓ Pass
T6 Incorrect query parameter handling ✓ Pass 6 9.4 ✗ Fail
T7 Off-by-one error in pagination ✓ Pass 2 2.8 ✓ Pass
T8 Unhandled promise rejection ✓ Pass 7 11.2 ✗ Fail
T9 Malformed JSON response structure ~ Partial 8 18.5 ~ Partial
T10 Incorrect route parameter extraction ~ Partial 6 12.1 ✗ Fail
The results reveal several patterns worth noting. First, Trace demonstrated particular strength on multi-file bugs. Of the six bugs involving two or more files, Trace successfully fixed four while the baseline fixed none. This aligns with the hypothesis that LSP-based navigation provides meaningful advantages when bugs span multiple code locations.
Second, for simpler single-file bugs (T2, T5, T7), both approaches showed comparable success, suggesting that the iterative verification loop provides the most value for complex debugging scenarios rather than straightforward fixes.
Third, the one failed task (T4) involved a concurrency issue that neither approach could diagnose from code inspection alone. This type of bug may require more sophisticated runtime analysis capabilities beyond what Trace currently provides.
5.1.3 Ablation Study: Contribution of LSP Navigation
To isolate the contribution of LSP-based tools, I ran a subset of five multi-file tasks with LSP navigation disabled. In this configuration, the agent could only use text-based search (FindTextInFiles) rather than semantic queries like GoToDefinition and FindAllReferences.
Table 5.3: LSP Navigation Ablation Results
Task With LSP Without LSP Iteration Difference
T1 ✓ (3 iterations) ✓ (8 iterations) +5
T3 ✓ (2 iterations) ✗ (limit reached) LSP required
T6 ✓ (6 iterations) ✓ (10 iterations) +4
T8 ✓ (7 iterations) ~ Partial Degraded outcome
T9 ~ Partial ✗ (limit reached) LSP required
Disabling LSP navigation resulted in degraded performance across all tested tasks. Success rate dropped from 60% (3/5) to 20% (1/5), and even successful fixes required significantly more iterations. On task T3, which required finding a validation function defined in a separate utilities module, the agent without LSP failed entirely because it could not efficiently locate the relevant code.
The average iteration count for tasks that succeeded in both conditions increased from 4.0 with LSP to 9.0 without LSP, representing a 125% increase in agent effort. This finding supports the claim that semantic navigation tools meaningfully improve debugging efficiency.
5.1.4 Ablation Study: Contribution of Runtime Verification
To evaluate the Verify loop’s contribution, I ran five tasks without runtime feedback. In this configuration, the agent proposed fixes based solely on static reasoning and could not execute tests to validate its solutions.
Table 5.4: Runtime Verification Ablation Results
Task With Verify Without Verify Notes
T1 ✓ (correct fix) Proposed fix incorrect Fix was syntactically valid but wrong location
T2 ✓ (correct fix) ✓ (correct fix) Simple enough for static reasoning
T3 ✓ (correct fix) Proposed fix incomplete Missing edge case handling
T5 ✓ (correct fix) ✓ (correct fix) Trivial fix
T6 ✓ (correct fix) Proposed fix incorrect Wrong interpretation of bug cause
Without the Verify loop, the agent produced plausible-looking but incorrect fixes in 3 of 5 cases. These fixes were syntactically valid and addressed a reasonable interpretation of the bug, but they failed when actually executed. For example, on task T1, the agent without verification proposed a null check that was placed in the wrong middleware function—the fix looked reasonable but did not resolve the failing test.
This finding provides strong support for H1’s claim that runtime feedback catches errors that static reasoning misses. The Verify loop serves as a critical guardrail that prevents the agent from confidently proposing incorrect solutions.
5.1.5 Match-and-Replace Edit Safety Analysis
Across all benchmark runs, the agent attempted 47 edit operations using the match-and-replace mechanism. The system rejected 6 proposed edits (12.8%) due to context mismatch, where the agent’s stated original code did not match the actual file contents.
Table 5.5: Edit Operation Analysis
Edit Outcome Count Percentage
Successfully applied 41 87.2%
Rejected (context mismatch) 6 12.8%
Of rejected: Agent self-corrected on retry 5 83.3%
Of rejected: Required additional file read 1 16.7%
In 5 of the 6 rejection cases, the agent successfully self-corrected by re-reading the target file and proposing a corrected edit on the next iteration. This demonstrates the mechanism working as intended—catching hallucinated or outdated code references before they corrupt the codebase.
Without this guardrail, these 6 edits would have either failed silently (if the target string was not found) or potentially modified unintended code locations (if partial matches occurred). The match-and-replace mechanism thus contributes meaningfully to edit safety, supporting H3.
5.1.6 Summary of Effectiveness Findings
The debugging effectiveness evaluation provides support for H1. Trace achieved a 70% success rate compared to 30% for the one-shot baseline, with particularly strong performance on multi-file bugs. The ablation studies confirmed that both LSP navigation and runtime verification contribute meaningfully to this improvement. LSP tools reduced iteration counts and enabled success on tasks that failed without semantic navigation. The Verify loop prevented incorrect fixes from being accepted, catching errors that static reasoning alone would miss.

5.2 EFFICIENCY (RQ2, H2)
The second research question asked whether Trace’s integrated approach reduces the time and effort required for debugging compared to alternative methods. I evaluated efficiency through both the benchmark experiments and the user study.
5.2.1 Time-to-Fix Analysis
For the seven successfully fixed benchmark tasks, the median time to fix was 6.2 minutes, with a range from 1.2 minutes (T5, a trivial fix) to 11.2 minutes (T8, a complex async error spanning three files).
Table 5.6: Time-to-Fix Distribution for Successful Tasks
Time Range Tasks Percentage
< 3 minutes 2 28.6%
3-7 minutes 3 42.9%
7-12 minutes 2 28.6%
The time distribution shows that most bugs were resolved within 7 minutes, with more complex multi-file bugs requiring up to 12 minutes. These times include all agent reasoning, tool execution, and verification steps.
5.2.2 Iteration Efficiency
The iteration count provides a model-agnostic measure of debugging effort. Across successful tasks, the median iteration count was 4, with an interquartile range of 2 to 6.5.
Table 5.7: Iteration Count Analysis
Bug Complexity Median Iterations Range
Single-file bugs (n=4) 2.5 1-5
Multi-file bugs (n=3) 6 3-7
Overall (n=7) 4 1-7
Multi-file bugs required approximately 2.4 times as many iterations as single-file bugs on average. This is expected given the additional navigation and context-gathering steps required when bugs span multiple code locations.
5.2.3 User Study: Trace-Assisted vs. Manual Debugging
In the user study, 8 participants each completed two debugging tasks—one using Trace and one using their preferred manual approach (IDE debugger, print statements, etc.). Tasks were counterbalanced to control for learning effects.
Table 5.8: User Study Time Comparison
Participant Task A (Trace) Task B (Manual) Task A Type Preferred Method
P1 11 min 28 min Multi-file Trace
P2 8 min 15 min Single-file Trace
P3 14 min 22 min Multi-file Trace
P4 9 min 12 min Single-file Manual
P5 18 min 35 min Multi-file Trace
P6 7 min 14 min Single-file Trace
P7 12 min 19 min Multi-file Trace
P8 10 min 16 min Single-file Trace
Across all participants, the mean time-to-fix was 11.1 minutes with Trace compared to 20.1 minutes with manual debugging, representing a 45% reduction in debugging time. Seven of eight participants preferred using Trace for similar future tasks, with one participant (P4) expressing a preference for manual debugging due to familiarity with their existing workflow.
The difference was most pronounced for multi-file bugs, where Trace reduced debugging time by 52% on average. For single-file bugs, the improvement was smaller but still meaningful at 35%.
5.2.4 Tool Utilization Patterns
Analysis of agent logs revealed patterns in how Trace utilized different tools across debugging sessions.
Table 5.9: Tool Usage Frequency
Tool Total Calls Calls per Successful Task Calls per Failed Task
ReadFile 127 14.3 22.0
GoToDefinition 34 4.1 2.0
FindAllReferences 18 2.3 1.0
FindTextInFiles 42 4.7 8.0
ReplaceText 23 2.9 2.0
Verify 31 3.7 6.0
GetFileSymbols 19 2.4 3.0
Several patterns emerge from this data. First, successful debugging sessions showed higher usage of LSP-based semantic tools (GoToDefinition, FindAllReferences) compared to failed sessions, which relied more heavily on text-based search. This suggests that effective debugging correlates with leveraging semantic navigation rather than brute-force file searching.
Second, failed sessions showed higher Verify call counts, indicating repeated unsuccessful fix attempts. The agent continued iterating until reaching the iteration limit rather than converging on a solution.
Third, ReadFile calls were higher in failed sessions, suggesting the agent was “exploring” more broadly without making progress—a pattern that could be addressed through improved context management in future work.
5.2.5 Summary of Efficiency Findings
The efficiency evaluation provides partial support for H2. Trace reduced debugging time by 45% compared to manual methods in the user study, with even larger improvements for multi-file bugs. Iteration counts were reasonable (median 4 for successful tasks), and tool utilization patterns suggest that semantic navigation contributes to efficient bug resolution. However, for simple single-file bugs, the improvement over manual methods was more modest, and some participants expressed that the overhead of Trace might not be worthwhile for trivial fixes.

5.3 USABILITY AND SAFETY (RQ3, H3)
The third research question examined whether Trace’s developer-in-the-loop design is perceived as usable and trustworthy. I evaluated this through standardized usability instruments, custom survey questions, and qualitative feedback from user study participants.
5.3.1 System Usability Scale (SUS) Results
Participants completed the System Usability Scale questionnaire after using Trace. The SUS provides a score from 0 to 100, with scores above 68 considered above average usability.
Table 5.10: SUS Score Distribution
Participant SUS Score Interpretation
P1 82.5 Excellent
P2 75.0 Good
P3 70.0 Good
P4 57.5 Below Average
P5 85.0 Excellent
P6 72.5 Good
P7 77.5 Good
P8 67.5 Average
Mean 73.4 Good
Std Dev 8.7 -
The mean SUS score of 73.4 places Trace in the “Good” usability range, above the industry benchmark of 68. Seven of eight participants scored Trace at or above 67.5, indicating broadly acceptable usability. One participant (P4) scored Trace below average, citing unfamiliarity with the agent-based workflow and a preference for direct control over the debugging process.
5.3.2 NASA-TLX Workload Assessment
Participants also completed the NASA Task Load Index to assess perceived workload across six dimensions.
Table 5.11: NASA-TLX Scores (0-100 scale, lower is better)
Dimension Mean Std Dev Interpretation
Mental Demand 42.5 12.3 Moderate
Physical Demand 15.0 8.2 Low
Temporal Demand 35.0 14.1 Moderate
Performance 28.8 10.5 Good (self-assessed)
Effort 38.8 11.2 Moderate
Frustration 32.5 18.4 Low-Moderate
The workload scores indicate that using Trace imposed moderate mental demand and effort, which is expected for a debugging task. Frustration levels varied considerably across participants (SD = 18.4), with participants who encountered agent failures reporting higher frustration. Physical and temporal demands were appropriately low for a software development task.
5.3.3 Trust and Control Perception
Custom survey questions assessed participants’ trust in Trace and their sense of control over the debugging process.
Table 5.12: Trust and Control Survey Results (5-point Likert scale)
Statement Mean Std Dev
“I trusted the fixes Trace proposed” 3.9 0.8
“I felt in control of the debugging process” 4.1 0.6
“The diff review helped me understand the changes” 4.5 0.5
“I could intervene when the agent went off track” 3.8 0.9
“I would use Trace for future debugging tasks” 4.0 0.9
Participants reported high confidence in their control over the process (mean 4.1/5) and found the diff review feature particularly valuable (mean 4.5/5). Trust in proposed fixes was moderate (3.9/5), with several participants noting they appreciated being able to review changes before applying them. The ability to intervene mid-run received slightly lower scores (3.8/5), with some participants noting they were unsure when or how to provide feedback during agent execution.
5.3.4 Qualitative Feedback Themes
Analysis of post-session interviews revealed several recurring themes in participant feedback.
Table 5.13: Qualitative Feedback Themes
Theme Frequency Valence Representative Quote
LSP navigation appreciated 7/8 Positive “It found the function definition instantly—that would have taken me a few minutes to track down manually.”
Diff review valued 7/8 Positive “I liked that I could see exactly what it wanted to change before anything happened to my code.”
Verify step builds confidence 6/8 Positive “Knowing the test passed made me confident the fix was actually right, not just plausible.”
Agent loops frustrating 4/8 Negative “It kept trying variations of the same thing. I wished I could tell it to try a completely different approach.”
Reasoning transparency helpful 5/8 Positive “Seeing its thought process helped me understand why it was looking at certain files.”
Mid-run feedback unclear 3/8 Negative “I wasn’t sure when I should jump in or what kind of hints would actually help.”
Repro setup tedious 4/8 Negative “Setting up the reproduction steps took longer than I expected.”
The feedback reveals that Trace’s core design decisions—LSP integration, diff review, and runtime verification—were well-received by most participants. The main pain points centered on agent behavior during extended debugging sessions (repetitive attempts) and uncertainty about how to effectively guide the agent through mid-run feedback.
5.3.5 Edit Safety in Practice
Beyond the automated match-and-replace safeguard, participants’ behavior during diff review provided additional safety validation. Across 23 proposed patches reviewed by participants, 2 were rejected before application.
Table 5.14: Participant Edit Review Behavior
Outcome Count Percentage
Accepted without modification 18 78.3%
Accepted after minor review 3 13.0%
Rejected by participant 2 8.7%
In both rejection cases, participants identified issues the agent had missed—in one case, a fix that would have broken a different test case, and in another, a stylistic concern about code organization. This demonstrates that the human-in-the-loop review serves as an effective final safeguard beyond automated checks.
5.3.6 Summary of Usability and Safety Findings
The usability evaluation provides support for H3. The mean SUS score of 73.4 indicates good usability, and participants reported feeling in control of the debugging process. The diff review feature was particularly valued, with a mean rating of 4.5/5. The match-and-replace mechanism caught 12.8% of potentially erroneous edits automatically, and participant review caught an additional 8.7% of patches that should not have been applied. The main usability concerns centered on agent behavior during extended sessions and clarity around mid-run intervention—both areas identified for future improvement.

5.4 DISCUSSION
The results presented above provide evidence for the value of IDE-native debugging agents that combine semantic navigation, iterative verification, and mixed-initiative interaction. In this section, I interpret these findings, compare Trace with existing approaches, and discuss the implications for future tool design.
5.4.1 Interpretation of Results Relative to Hypotheses
H1 (Effectiveness) was supported. Trace achieved a 70% success rate on benchmark tasks compared to 30% for the one-shot baseline, with statistically significant improvement. The ablation studies confirmed that both LSP navigation and runtime verification contribute to this effectiveness—removing either component degraded performance substantially. The particularly strong results on multi-file bugs suggest that Trace’s architectural decisions provide the most value for non-trivial debugging scenarios where context gathering and iterative refinement are essential.
H2 (Efficiency) was partially supported. Trace reduced debugging time by 45% compared to manual methods in the user study, and median iteration counts were reasonable at 4 for successful tasks. However, the efficiency gains were more pronounced for complex bugs than simple ones. For trivial single-file fixes, some participants felt the overhead of interacting with an agent-based system outweighed the benefits. This suggests Trace is best suited for debugging tasks of moderate-to-high complexity.
H3 (Usability and Safety) was supported. SUS scores indicated good usability, and participants reported feeling in control of the process. The layered safety mechanisms—match-and-replace validation, runtime verification, and human diff review—each contributed to preventing incorrect fixes from being applied. Qualitative feedback confirmed that transparency features (reasoning visibility, step-by-step logging) enhanced rather than hindered the debugging experience.
5.4.2 Comparison with SWE-Agent
SWE-Agent represents the current state of the art in autonomous LLM-based debugging, achieving 12.5% success on the full SWE-bench dataset. While direct comparison is complicated by differences in evaluation setup (SWE-Agent targets Python repositories; Trace targets JavaScript/TypeScript backends), several architectural differences merit discussion.
Interface Philosophy. SWE-Agent introduces a specialized Agent-Computer Interface (ACI) optimized for LLM interaction, including custom shell commands and structured output formats. Trace instead leverages existing IDE capabilities through LSP, which provides semantic code understanding without requiring custom interface design. The LSP approach offers broader language support (any language with an LSP server) but may sacrifice some optimization opportunities that a bespoke interface could provide.
Execution Environment. SWE-Agent operates headlessly in Docker containers, providing isolation but disconnecting from the developer’s workflow. Trace embeds directly in VS Code, enabling real-time developer interaction but requiring the developer to maintain their local environment. This trade-off reflects fundamentally different use cases: SWE-Agent targets fully autonomous repair, while Trace targets developer-assisted debugging.
Verification Approach. Both systems use test execution for validation, but with different integration depths. SWE-Agent runs tests as shell commands through its ACI. Trace’s Verify tool provides structured feedback including readiness detection, log parsing, and timeout handling. The structured approach may provide richer information for agent reasoning, though I did not directly compare verification effectiveness.
Developer Interaction. The most significant difference is Trace’s mixed-initiative design. SWE-Agent runs autonomously until completion or failure. Trace allows developers to provide mid-run feedback, review intermediate results, and approve final patches. The user study results suggest this interaction model is valued—participants appreciated feeling “in the loop” rather than watching a black box operate.
Python Limitation. A practical consideration is that SWE-Agent currently supports only Python repositories, while Trace supports any language with LSP and appropriate test infrastructure. This limitation prevented direct benchmarking on identical tasks, but it also highlights Trace’s broader applicability to real-world polyglot development environments.
5.4.3 Comparison with IDE-Based AI Tools
Modern IDEs increasingly embed AI capabilities, from GitHub Copilot’s code completion to VS Code’s agent mode with tool calling support. Trace occupies a different position in this landscape.
Scope of Assistance. Copilot and similar tools excel at code generation and completion—providing suggestions as developers type. They are reactive, responding to the immediate context around the cursor. Trace is proactive and task-oriented, taking a bug description and autonomously working toward resolution through multiple steps. The tools serve complementary rather than competing purposes.
Debugging Capability. Current IDE AI tools provide limited debugging support. Copilot can suggest fixes when shown error messages, but it lacks the ability to navigate codebases, run tests, or iterate based on feedback. Trace fills this gap by integrating these capabilities into a coherent debugging workflow. User study feedback confirmed that participants found Trace useful for tasks where Copilot-style assistance would be insufficient.
Transparency. IDE AI tools typically operate as “ghost text”—suggestions appear and the developer accepts or rejects them without insight into why particular code was suggested. Trace’s explicit reasoning traces and step-by-step logging provide transparency that some participants specifically praised. This transparency may be particularly valuable in educational contexts or when debugging unfamiliar codebases.
Integration Depth. Trace is implemented as a VS Code extension and inherits the IDE’s capabilities (syntax highlighting, Git integration, terminal access). However, it operates somewhat separately from the main editing experience through its sidebar panel. Deeper integration—such as inline diff previews or code lens annotations—could improve the user experience in future versions.
5.4.4 Comparison with Autonomous Repair Agents
RepairAgent and similar systems represent a different approach to LLM-based debugging, using finite state machines and specialized tool suites optimized for program repair.
Specialization vs. Generality. RepairAgent is purpose-built for code repair, with tools specifically designed for fault localization, patch generation, and validation. Trace is more general, supporting any debugging or development task where verification is possible. This generality comes at the cost of some optimization—Trace may use more LLM tokens per bug than a specialized repair agent.
Autonomy Level. RepairAgent operates fully autonomously, making all tool selection and planning decisions without human input. Trace maintains human oversight through mid-run feedback and final approval. The user study suggests developers value this oversight, at least in current LLM capability regimes where agent decisions are not fully reliable.
Benchmark Performance. RepairAgent reports fixing 164 bugs on Defects4J, achieving state-of-the-art results on that benchmark. Trace’s 70% success rate on a smaller custom benchmark is not directly comparable, but it demonstrates competitive effectiveness for the targeted use case of backend JavaScript/TypeScript debugging.
5.4.5 Design Implications
The evaluation results suggest several principles for designing developer-centered debugging agents.
Semantic navigation matters. The ablation study showed that removing LSP-based tools significantly degraded both success rates and efficiency. Future debugging agents should integrate deeply with language tooling rather than relying solely on text-based search.
Runtime verification is essential. Without the Verify loop, the agent produced plausible but incorrect fixes in the majority of cases. Any debugging agent that lacks execution feedback risks confidently proposing broken solutions.
Transparency builds trust. Participants valued being able to see agent reasoning and review proposed changes. Black-box operation may be acceptable for code completion but appears insufficient for debugging tasks where incorrect fixes have higher stakes.
Human oversight remains valuable. Despite Trace’s automated safeguards (match-and-replace validation, test verification), participant review caught additional issues. The human-in-the-loop design provides a valuable final layer of quality control.
Complexity determines value. The efficiency benefits of Trace were most pronounced for multi-file bugs requiring cross-module navigation. For trivial fixes, traditional methods may be faster. This suggests debugging agents should be positioned as tools for complex tasks rather than replacements for all debugging activity.
5.4.6 Threats to Validity
Several limitations affect the interpretation of these results.
Internal Validity. The user study employed counterbalancing to control for learning effects, but with only 8 participants, individual variation may influence results. The benchmark tasks were curated by the researcher, which could introduce selection bias toward bugs that favor Trace’s design.
External Validity. The evaluation focused on backend JavaScript/TypeScript systems with Express-style routing. Results may not generalize to frontend applications, different programming languages, or bugs without clear test cases. The participant sample consisted of graduate students and early-career developers; more experienced developers might interact with Trace differently.
Construct Validity. Success was defined as passing all provided test cases, but test suites may be incomplete. A fix that passes tests might not be the “correct” fix from a code quality or maintainability perspective. SUS and NASA-TLX are validated instruments, but they measure general usability rather than debugging-specific effectiveness.
Conclusion Validity. The sample sizes (10 benchmark tasks, 8 user study participants) provide limited statistical power. The significant result for H1 should be interpreted cautiously, and the efficiency and usability findings are better viewed as directional evidence than definitive conclusions.
5.4.7 Limitations
Beyond validity threats, several practical limitations constrain Trace’s current applicability.
Reproduction Step Requirement. Trace requires users to configure reproduction steps before debugging can begin. This setup burden was noted as a pain point by user study participants and may limit adoption for quick debugging tasks.
Single Session Constraint. The current implementation supports only one debugging session at a time, preventing parallel investigation of related issues.
Model Dependency. Trace’s effectiveness depends on the underlying LLM’s capabilities. Results with GPT-4 may not transfer to other models, and API costs for extended debugging sessions could be prohibitive in some contexts.
Context Window Growth. Long debugging sessions risk exceeding the model’s context window. While history pruning partially addresses this, very complex bugs may still cause degraded performance as context accumulates.
Concurrency Limitations. As demonstrated by the failed benchmark task (T4), Trace struggles with bugs that require understanding concurrent or asynchronous execution patterns. Runtime logging provides some insight, but the agent lacks the ability to set breakpoints or inspect execution state at specific points.

5.5 SUMMARY
This chapter presented the evaluation of Trace across three dimensions. For debugging effectiveness, Trace achieved 70% success on benchmark tasks compared to 30% for a one-shot baseline, with ablation studies confirming the contribution of LSP navigation and runtime verification. For efficiency, Trace reduced debugging time by 45% compared to manual methods, with larger gains on complex multi-file bugs. For usability and safety, participants rated Trace’s usability as good (SUS 73.4), valued the diff review and transparency features, and the layered safety mechanisms prevented incorrect fixes from being applied.
Comparison with existing tools reveals that Trace occupies a distinctive position—more autonomous than IDE code assistants, more developer-integrated than headless agents like SWE-Agent, and more transparent than specialized repair systems. The evaluation supports the hypothesis that IDE-native debugging agents with semantic navigation, iterative verification, and mixed-initiative interaction can meaningfully improve developer productivity on non-trivial debugging tasks.
The following chapter presents concluding remarks and directions for future work.
