Paper Info
Title
Automated Classification of Class Role-Stereotypes via Machine Learning
Abstract
Role stereotypes indicate generic roles that classes play in the design of software systems (e.g. controller, information holder, or interfacer). Knowledge about the role-stereotypes can help in various tasks in software development and maintenance, such as program understanding, program summarization, and quality assurance. This paper presents an automated machine learning-based approach for classifying the role-stereotype of classes in Java. We analyse the performance of this approach against a manually labelled ground truth for a sizable open source project (of 770+ Java classes) for the Android platform. Moreover, we compare our approach to an existing rule-based classification approach. The contributions of this paper include an analysis of which machine learning algorithms and which features provide the best classification performance. This analysis shows that the Random Forest algorithm yields the best classification performance. We find however, that the performance of the ML-classifier varies a lot for classifying different role-stereotypes. In particular its performs degrades for rare role-types. Our ML-classifier improves over the existing rule-based classification method in that the ML-approach classifies all classes, while rule-based approaches leave a significant number of classes unclassified.
Year
DOI
Venue
2019
10.1145/3319008.3319016
Proceedings of the Evaluation and Assessment on Software Engineering
Keywords
Field
DocType
Program Understanding, Reverse Engineering, Software Design
Automatic summarization,Android (operating system),Software design,Computer science,Reverse engineering,Software system,Artificial intelligence,Random forest,Java,Software development,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-7145-2
2
0.40
References 
Authors
0
3
Name
Order
Citations
PageRank
Arif Nurwidyantoro172.81
Truong Ho-Quang2345.91
Michel R. V. Chaudron369366.15