ML Code Maintainability

• Code Maintainability in Machine Learning is defined as the ability to understand, modify, test, and evolve complex ML codebases using metrics like cyclomatic complexity and maintainability index.
• Adoption of OOP, SOLID principles, and design patterns such as Factory and Observer improves modularity, reduces coupling, and enhances code clarity in ML pipelines.
• Emerging ML-based refactoring techniques, including LLM-driven and GNN approaches, measurably reduce technical debt and improve maintainability through automated analysis.

Machine-learning code maintainability encompasses the properties and engineering practices that enable ML codebases to be clearly understood, modified, tested, evolved, and debugged over their lifecycle. Unlike traditional software, ML projects are shaped by data dependencies, fluid model architectures, and fast-evolving pipelines—posing unique challenges beyond standard modularity and testability. Emerging research identifies specific design patterns, static and learning-based metrics, workflow best practices, code smells, and automation tools that, when systematically employed, can substantially improve the maintainability of ML code across pipeline stages and project scales.

1. Foundational Concepts and Metrics

Software maintainability in ML is defined as the degree to which code can be readily understood, modified, extended, evolved, and debugged without introducing errors or excessive effort. Formally, maintainability metrics applied to ML code include:

• Cyclomatic Complexity (CC): The number of independent control-flow paths, with the standard formula $C = E - N + 2P$ evaluating edges $E$, nodes $N$, and connected components $P$ in a control-flow graph.
• Halstead Volume and Effort: Cognitive complexity captured as $V = N\log_2\eta$, $E = D\times V$ using operator/operand counts.
• Maintainability Index (MI): Composite metric—e.g., $$MI = \max\{0, 100 \cdot (171-5.2\ln(V)-0.23CC-16.2\ln(LOC)+50\sin(\sqrt{2.4C}))/171\}$$—with higher values implying superior maintainability (Shivashankar et al., 2024).
• Cohesion/Coupling: Cohesion $Ch$ quantified via method-attribute interaction ratios; coupling $Cp$ via module interdependency matrices ($Cp = \sum_{i=1}^M \sum_{j=1}^N U_{ij}$) (Wang et al., 2024).

Metrics are computed statically (e.g., Radon, Pylint, CodeScene), by ML models trained on expert scores (Borg et al., 2024), or via code property graphs. High maintainability associates with low CC, high MI/cohesion, and minimal coupling.

2. Design Principles and Patterns in ML Maintainability

Object-oriented and modular design are central to maintainable ML code:

• OOP Principles: Encapsulation, inheritance, polymorphism, and abstraction structure ML logic into distinct, testable units (e.g., DataLoader, PreProcessor, Model, Trainer, Evaluator) (Wang et al., 2024).
• SOLID Principles:
  • SRP: One responsibility per class.
  • OCP: Open for extension, closed for modification.
  • LSP: All model subclasses share a fit/predict interface.
  • ISP: Segregated interfaces for evaluation or transformation roles.
  • DIP: High-level pipeline logic depends on abstractions, not implementations.
  • Controlled experiments demonstrate that SOLID refactorings statistically improve code understanding and maintainability (e.g., $p<0.01$ for multiple metrics with Cohen's $d>1$ effect sizes) (Cabral et al., 2024).
• Design Patterns: Factory for model instantiation, Strategy for preprocessing, Template Method for training scaffolds, Observer for decoupled logging/metrics, and composition-oriented approaches generally reduce complexity and coupling (Wang et al., 2024).
• Model-Driven Engineering (MDE): SysML-based pipeline modeling and template-driven code generation support modular, evolvable ML architectures, isolating code generation and facilitating maintainable extensions (Raedler et al., 2023).

3. Empirical Analysis: Code Smells, Technical Debt, and Tool Support

Sustained analyses of ML repositories have mapped recurring code smells and technical debt drivers to specific pipeline phases:

• Code Smells in ML:
  • Experimental duplication and monolithic "god files" arise frequently (duplication density $\delta > 0.05$ is common) (Oort et al., 2021, Gesi et al., 2022).
  • Poor abstractions: Long methods, long parameter lists, improper metrics, and scattered API usage undermine responsibility separation and modularity (Cardozo et al., 2023, Gesi et al., 2022).
  • ML-specific smells cataloged in (Zhang et al., 2022): e.g., unnecessary iteration, failure to control randomness, data leakage, missing mask for invalid values, API misuse, threshold-dependent validation.
  • Code smell densities up to 3.95% of LOC in RL code, with long method and message chains particularly prevalent (Cardozo et al., 2023).
• Technical Debt in ML:
  • Most critical issues cluster in data pre-processing, with shortcuts and "patch fixes" in feature selection, missing-value handling, scaling, and outlier detection generating persistent technical debt (Ximenes et al., 18 Feb 2025).
  • Debt metrics: Patch complexity $C_{total} = C_0 + \delta n$, error propagation $\epsilon_{model} \approx \alpha\epsilon_{data}$, duplication and test coverage metrics.
  • Mitigation requires centralizing data integration, encapsulating preprocessing, automating schema/contract validation, and tracking metrics via code reviews and CI (Ximenes et al., 18 Feb 2025).

Category	Key Smells/Issues	Best Practice(s)
Data handling	Unnecessary iteration, data leaks	Vectorization, pipeline abstraction, isolated test data
Modeling	Long methods, monolithic scripts	OOP decomposition, modular classes, controller pattern
Automation	Lack of reproducibility, missing logs	Experiment tracking, configuration files, seed management

4. Automated Maintainability Assessment: ML Models and Code Smell Detection

Recent work formalizes automated and ML-based maintainability assessment tools:

• Static Analyzers: SonarQube and CodeScene provide maintainability indices and code smell diagnoses, but SonarQube's maintainability ratio can yield high false positives—CodeScene aligns better with human expert consensus (AUC = 0.95–0.97) and provides actionable suggestions (e.g., extract method or parameter object) (Borg et al., 2024).
• MLRefScanner: Classifies Python ML commit-level refactoring using LightGBM ensembles trained on textual, process, and code metrics, capturing both generic and ML-specific refactoring patterns. Achieves 94% precision/82% recall, outperforming rule-based PyRef’s 33% recall. Hybrid ensemble model (MLRefScanner+PyRef) achieves F1 ≈ .97, enabling both high coverage and interpretability (Noei et al., 2024).
• GNN-Based Refactoring: Graph neural networks (GCN) applied to AST graphs enable context-aware refactoring suggestions. Such models reduce cyclomatic complexity by 35% and coupling by 33%, substantially outperforming static rule-based tools and shallow ML models (Bandarupalli, 14 Apr 2025).
• LLM-based Maintainability Evaluation: Cross-entropy scores from LLMs—if normalized for code size—predict expert-rated maintainability (negative coefficient, $p=0.009$ with size correction), but add little signal beyond logical lines of code. Aggregation with static metrics is recommended for practical use (Dillmann et al., 2024).
• Clone Validation: ML-based validators automatically filter spurious code clones reported by legacy tools, reducing manual review effort by 60–70% and improving refactoring consistency (Mostaeen et al., 2020).

5. AI-Assisted Refactoring and Declarative Abstraction

• LLM-Driven Refactoring: Fine-tuned LLMs (e.g., WizardCoder, GPT-3.5) trained with maintainability-focused prompts measurably reduce SLOC, halstead effort, and cyclomatic complexity while increasing maintainability indices. For small-scale Python, mean CC is reduced by ~19%, MI increased by 1–2.5%. Functional correctness remains preserved (BERTScore F1 ≈ 0.90–0.94). However, most current LLMs, when prompted for real-world bugfixes, introduce new errors or require careful human oversight—only 44.9% (few-shot) to 32% (zero-shot) of maintainability fixes succeed in real-world Java projects, with notable rates of compilation/test failures (Shivashankar et al., 2024, Nunes et al., 4 Feb 2025).
• LLM-Aided Declarative Abstraction: LLMs can rewrite messy imperative ML code (ad hoc loops, joins, manual preprocessing) to declarative, provenance-rich DataFrame and transformation APIs (e.g., Lester system), dramatically enhancing maintainability, readability, modularity, and enabling incremental view maintenance and compliance workflows (Schelter et al., 2024).

6. Empirical Effects and Project Best Practices

Comprehensive studies document that systematic adoption of OOP, design patterns, modularization, and code smell reduction yields:

• sharply reduced cyclomatic complexity, coupling, and code duplication (e.g., C drops by 51%, Cp by 58%, Ch increases by 88% post-OOP refactoring) (Wang et al., 2024);
• higher onboarding speed and lower defect rates in ML codebases (e.g., 30% faster, 50% fewer bugs) (Wang et al., 2024);
• improved code understanding and readability as verified by controlled experiments (e.g., SRP clarity improvement with $d=1.6$, $p<0.01$) (Cabral et al., 2024);
• maintainability bottlenecks in RL and DL traced to long monolithic methods, large parameter lists, and entangled abstractions (Cardozo et al., 2023, Gesi et al., 2022).

Essential best practices include: centralized data integration and contract checks, encapsulating preprocessing/transformation steps, isolating test/validation splits, pinning hyperparameters and random seeds, explicit versioning and documentation, modular project layout, and continuous integration checks for both code smells and technical debt (Ximenes et al., 18 Feb 2025, Wang et al., 2024, Zhang et al., 2022).

7. Open Challenges and Future Directions

While automated and ML-based maintainability assessment has advanced, key open research challenges include:

• Extension from Python/Java to broader language/tooling ecosystems (e.g., robust cross-language feature engineering, AST modeling) (Noei et al., 2024, Bandarupalli, 14 Apr 2025).
• Integration of semantic and architectural (cohesion/testability) features into learning-based predictors (Bandarupalli, 14 Apr 2025).
• Reducing the need for human-in-the-loop correction and handling of edge cases or context drift in LLM-driven refactoring (Shivashankar et al., 2024, Schelter et al., 2024).
• Benchmarking and grounding code quality research in diverse, human-labeled datasets tied to ML domain specifics, not only classical software systems (Borg et al., 2024, Dillmann et al., 2024).
• Formalizing maintainability as both a metric set (complexity, cohesion, coupling, reproducibility) and as a lifecycle property encompassing technical debt management (Ximenes et al., 18 Feb 2025).

A plausible implication is that hybrid approaches—combining AST-driven graph models, rule-based tools, expert-labeled benchmarks, and best-practice–oriented LLM prompting—offer the most promise for future-proof ML code maintainability. Empirical evaluations and ML-aware static analysis must continue to adapt to the evolving landscape of frameworks, team workflows, and regulatory requirements across the ML lifecycle.