In the fast-paced world of software development, codebases evolve rapidly. Over time, many applications accumulate code smells — subtle indicators of deeper structural issues that make the software harder to maintain, extend, and debug. These aren’t bugs that break your program outright but warning signs of poor design decisions.
While code smells are traditionally detected through manual reviews or static analysis tools, Machine Learning (ML) offers a promising new way to automate and improve this detection — especially in large, complex legacy software systems where human review is time-consuming and error-prone.
In this blog, we’ll explore how ML can be applied to detect code smells in legacy systems, the challenges involved, and how you can start building your own ML-based code smell detector.
What Are Code Smells?
Code smells are patterns in code that may indicate potential issues or poor practices. Common examples include:
- Long Methods
- Large Classes
- Duplicate Code
- God Classes (classes that do too much)
- Feature Envy (methods that access data from other classes excessively)
These smells increase technical debt and reduce the maintainability and readability of software.
Why Legacy Systems?
Legacy systems often:
- Contain outdated code written years or decades ago.
- Lack proper documentation.
- Have been maintained by many developers over time.
- Are critical to business operations.
Detecting and addressing code smells in such systems can dramatically improve software health and reduce maintenance costs.
Traditional Methods vs Machine Learning
| Traditional Static Analysis | ML-based Detection |
|---|---|
| Rule-based, limited to predefined patterns. | Learns patterns from data, adaptable to complex or custom smells. |
| Requires manual updates to rules. | Can continuously improve with new data. |
| Misses context-specific issues. | Can capture contextual relationships in code. |
How Machine Learning Detects Code Smells
Feature Extraction
To train an ML model, you need to convert code into numerical features. Possible features include:
- Lines of code (LOC)
- Number of methods in a class
- Number of attributes
- Cyclomatic complexity
- Number of nested loops/if statements
- Coupling between classes
Optionally, use Abstract Syntax Trees (ASTs) to parse and extract structural information from the code.
Model Selection
For initial experiments:
- Classification Models: Random Forest, Decision Trees, SVM.
- Neural Approaches: Code2Vec, Graph Neural Networks (GNNs) for better structural representation.
Each code snippet or class becomes a feature vector, and the model classifies whether it contains a specific smell.
Sample Workflow
- Collect and label code snippets (either manually or using static analysis tools as a baseline).
- Extract features from code (using metrics, ASTs, or embeddings).
- Train an ML model on the labeled data.
- Evaluate model performance (accuracy, precision, recall, F1-score).
- Deploy as a part of your CI/CD pipeline or IDE plugin.
Challenges
- Data scarcity: No large, open-source datasets of labeled code smells.
- Imbalanced classes: Some smells (like God Classes) are rarer than others.
- Context awareness: Some smells depend on usage context, not just structure.
- Tooling gaps: Few libraries are tailored for ML-based code smell detection.
Existing Tools & Libraries
- AST parsers:
tree-sitter,javalang,antlr4 - ML libraries: Scikit-learn, PyTorch, TensorFlow
- Code Embedding Models: Code2Vec, CodeBERT, GraphCodeBERT
Future Possibilities
- Integrating ML-based smell detection into IDEs (like Visual Studio Code or IntelliJ).
- Building models that not only detect smells but suggest refactoring.
- Creating open-source, language-agnostic smell detection datasets.
Conclusion
Machine Learning opens up exciting possibilities in automating code quality assessment, especially for unwieldy, critical legacy systems. While challenges remain — particularly around data availability and model interpretability — the potential benefits in terms of maintainability and reduced technical debt are significant.
If you’re passionate about ML and software engineering, this niche is ripe for exploration, innovation, and impact.
Bonus: Prototype Idea
Build a simple Random Forest-based classifier for detecting Long Methods in Java code using javalang for parsing and Scikit-learn for modeling. Publish the source code, and you’ll likely be one of the few doing it.