Abstract
Many developers leverage large language models to write brand-new source code. Nevertheless, real engineering workloads consist predominantly of maintaining legacy code: supplementing incomplete comments, refactoring tangled logic, and mapping cross-module dependency chains. This article presents two weeks of controlled testing that evaluates GPT-5.6 against Claude 4.8, Gemini 2.5 Pro and Grok 4.3 across three core maintenance scenarios. All comparative trials were arranged via Treerouter for standardized prompt and environment control. The test focuses on medium-sized code modules written in Python, TypeScript and Go, quantifying each model’s strengths, blind spots and operational risks during day-to-day repository upkeep. The outcome confirms GPT-5.6 delivers standout performance on dependency analysis and legacy code comprehension, while teams should adopt hybrid model strategies for large-scale refactoring tasks.
1. Experiment Setup
The testing framework was designed to mirror typical enterprise maintenance workflows.
| Item | Specification |
|---|---|
| Primary test model | GPT-5.6 (latest OpenAI iteration) |
| Baseline competitors | Claude 4.8, Gemini 2.5 Pro, Grok 4.3 |
| Supported programming languages | Python, TypeScript, Go |
| Core evaluation scenarios | Comment completion, code refactoring, dependency analysis |
| Target code scale | Medium modules (500–2000 lines) |
Medium-sized modules were chosen as the test subject. Tiny code fragments fail to expose inter-module reasoning gaps, while extremely large repositories exceed the practical context window limits used in daily developer prompting.
2. Comment Completion: Beyond Simple Descriptive Annotations
An 800-line Python data processing module with almost zero native comments was used for this test.
GPT-5.6 completes three layers of annotation work: function-level docstrings, inline commentary for branching logic, and module usage guidance. Its output avoids superficial descriptions such as “this function processes data”. Instead, it clearly defines input/output schemas, boundary constraints and exception scenarios.
Horizontal comparison:
- Claude 4.8 generates more concise annotations, suited for projects that pursue minimalist coding style;
- Gemini and Grok tend to produce verbose comments with low density of actionable information.
A notable strength of GPT-5.6: it infers business semantics from function naming and surrounding context. Annotations explain not only what the code executes, but also why the logic was implemented in this way, which significantly lowers the learning curve for engineers taking over unfamiliar projects.
⚠️ Critical limitation: The model occasionally misinterprets domain logic. Comments may appear logically consistent yet contradict real business rules. Any annotation touching industry terminology must be manually audited.
3. Refactoring Capability: Noticeable Improvements Over Prior Generations
Earlier GPT versions frequently suffered from over-optimization, unnecessarily complicating straightforward logic. This weakness has been largely mitigated within GPT-5.6. The test material is a 1200-line TypeScript API service carrying legacy technical debt: duplicated error handling, hardcoded configuration entries and scattered logging routines.
| Refactoring Dimension | GPT-5.6 Performance | Peer Reference |
|---|---|---|
| Duplicated logic extraction | Accurately identifies reusable logic and extracts shared functions | Claude 4.8 achieves similar quality |
| Externalize hardcoded configuration | Splits parameters reasonably without breaking existing invocations | Gemini occasionally omits relevant entries |
| Unified error handling | Creates layered, orderly exception workflows | Grok shows weaker stability |
| Preserve logical equivalence | Marks all locations with functional changes | Shared advantage with Claude 4.8 |
One practical merit of its refactoring strategy: it retains original function signatures for backward compatibility while implementing updated internal logic. This incremental approach prevents cascading failures for upstream callers.
Tradeoff to watch: GPT-5.6 sometimes expands the scope of refactoring beyond the requested boundaries. If instructed to adjust error handling, it may simultaneously rewrite unrelated logging pipelines. Engineers should request a formal refactoring plan and conduct reviews before executing modifications.
4. Dependency Analysis: GPT-5.6’s Strongest Capability
This scenario creates the clearest performance gap between GPT-5.6 and competing models. A Python project containing 15 interdependent modules was provided, with the task of mapping module relationships, detecting circular dependencies and flagging overlong dependency chains.
GPT-5.6 identified three circular dependency paths, two unnecessary cross-layer invocations and one module suitable for independent splitting. The quality of this analysis matches reviews performed by senior engineers familiar with the repository.
Other models show visible gaps:
- Claude 4.8 can conduct dependency mapping, yet struggles to detect implicit dependencies triggered via global variables;
- Gemini and Grok only recognize direct import relationships and miss indirect runtime coupling.
An exclusive feature observed in GPT-5.6: it calculates a qualitative “module health score” derived from dependency topology, flagging highly coupled components and modules with unfocused responsibilities. This metric delivers high reference value for long-term repository governance.
5. Model Selection Guidance for Typical Maintenance Tasks
| Maintenance Workload | Primary Recommendation | Alternative Option |
|---|---|---|
| Supplementary comment writing | GPT-5.6 | Claude 4.8 |
| Large-scale systematic refactoring | Claude 4.8 | GPT-5.6 |
| Cross-module dependency auditing | GPT-5.6 | Claude 4.8 |
| Legacy code comprehension | GPT-5.6 | Claude 4.8 |
| Interface-compatible iterative refactoring | GPT-5.6 | Gemini 2.5 Pro |
| Quick hotfix & fault repair | Claude 4.8 | GPT-5.6 |
The test data demonstrates GPT-5.6 excels at understanding aged legacy code, making it ideal for repositories with multi-year accumulated technical debt. Claude 4.8 delivers cleaner execution during large-scale refactoring. A hybrid combination of the two yields the best overall maintenance efficiency.
6. Practical Engineering Recommendations
- Attach full project context alongside source code. Identical functions produce drastically different results when supplemented with business background such as “e-commerce order settlement system”. GPT-5.6 leverages contextual information far more effectively than earlier iterations.
- Supply related modules as input for dependency analysis. A single source file cannot expose cross-file coupling; for projects exceeding 15 modules, execute analysis in batches.
- Complete manual verification after comment generation. Plausible-looking annotations may contain flawed domain reasoning.
- Request a written refactoring blueprint before allowing mass code modification. Directly granting write permissions risks overreach; phased execution improves safety for large refactors.
- Treat model-generated performance estimations cautiously. Qualitative judgements about bottlenecks can be referenced, but quantitative latency predictions are frequently unreliable.
7. Conclusion
GPT-5.6 achieves measurable progress for code maintenance workflows compared with prior generations. Its advantages are most prominent in dependency topology analysis and understanding undocumented legacy systems. While annotation output quality is high, human validation remains mandatory. Its refactoring capability has matured, yet developers must constrain the scope of changes.
No single model dominates every scenario. Claude 4.8 remains more stable for massive refactoring and rapid hotfixes. For teams sustaining long-lived repositories with heavy legacy code, GPT-5.6’s ability to untangle undocumented logic creates tangible engineering value.
For practitioners, code maintenance is an ongoing, iterative workload. Choosing the right LLM reduces repeated manual investigation. When uncertain about model suitability for your internal codebase, standardized comparative testing under real task prompts is more reliable than relying solely on public leaderboard rankings.
