To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes

TL;DR

This paper presents a Naive Bayes classifier that automates mapping source code entities to architectural modules using semantic and syntactic features, outperforming existing methods in Java systems.

Contribution

It introduces a machine learning approach with Naive Bayes for automatic code-to-architecture mapping, reducing parameterization and improving accuracy over state-of-the-art techniques.

Findings

Naive Bayes classifier outperforms existing mapping functions

The approach requires fewer parameters and is more effective

It provides a useful baseline for semi-automatic clustering

Abstract

Background: The process of mapping a source code entity onto an architectural module is to a large degree a manual task. Automating this process could increase the use of static architecture conformance checking methods, such as reflexion modeling, in industry. Current techniques rely on user parameterization and a highly cohesive design. A machine learning approach would potentially require fewer parameters and better use of the available information to aid in automatic mapping. Aim: We investigate how a classifier can be trained to map from source code to architecture modules automatically. This classifier is trained with semantic and syntactic dependency information extracted from the source code and from architecture descriptions. The classifier is implemented using multinomial naive Bayes and evaluated. Method: We perform experiments and compare the classifier with three state-of-the-art mapping functions in eight open-source Java systems with known ground-truth-mappings. Results: We find that the classifier outperforms the state-of-the-art in all cases and that it provides a useful baseline for further research in the area of semi-automatic incremental clustering. Conclusions: We conclude that machine learning is a useful approach that performs better and with less need for parameterization compared to other approaches. Future work includes investigating problematic mappings and a more diverse set of subject systems.