# Feature Maps: A Comprehensible Software Representation for Design Pattern Detection

arXiv: https://arxiv.org/abs/1812.09873 · HTML: https://mlsanity.com/paper/1812.09873/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [GEML: A Grammar-based Evolutionary Machine Learning Approach for Design-Pattern Detection](https://mlsanity.com/paper/2401.07042/) (2024)
- [LLM-Based Design Pattern Detection](https://mlsanity.com/paper/2502.18458/) (2025)
- [Introduction to Analytical Software Engineering Design Paradigm](https://mlsanity.com/paper/2505.11979/) (2025)

## Abstract (arXiv)

Design patterns are elegant and well-tested solutions to recurrent software development problems. They are the result of software developers dealing with problems that frequently occur, solving them in the same or a slightly adapted way. A pattern's semantics provide the intent, motivation, and applicability, describing what it does, why it is needed, and where it is useful. Consequently, design patterns encode a well of information. Developers weave this information into their systems whenever they use design patterns to solve problems. This work presents Feature Maps, a flexible human- and machine-comprehensible software representation based on micro-structures. Our algorithm, the Feature-Role Normalization, presses the high-dimensional, inhomogeneous vector space of micro-structures into a feature map. We apply these concepts to the problem of detecting instances of design patterns in source code. We evaluate our methodology on four design patterns, a wide range of balanced and imbalanced labeled training data, and compare classical machine learning (Random Forests) with modern deep learning approaches (Convolutional Neural Networks). Feature maps yield robust classifiers even under challenging settings of strongly imbalanced data distributions without sacrificing human comprehensibility. Results suggest that feature maps are an excellent addition in the software analysis toolbox that can reveal useful information hidden in the source code.