Detecting Design Patterns in Object-Oriented Program Source Code by Using Metrics and Machine Learning
| Autor: | Satoru Uchiyama, Yoshiaki Fukazawa, Hironori Washizaki, Atsuto Kubo |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Object-oriented programming
Class (computer programming) Source code business.industry Computer science Design pattern media_common.quotation_subject Static analysis Machine learning computer.software_genre Software metric Software design pattern Structural pattern Data mining Artificial intelligence business computer media_common |
| Zdroj: | Journal of Software Engineering and Applications. :983-998 |
| ISSN: | 1945-3124 1945-3116 |
| DOI: | 10.4236/jsea.2014.712086 |
| Popis: | Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques; however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|