ACADSTAFF UGM
| Title | : | Role stereotypes in software designs and their evolution |
| Author | : |
Truong Ho-Quang (1) Arif Nurwidyantoro, S.Kom., M.Cs., Ph.D. (2) Satrio Adi Rukmono (3) Michel RV Chaudron (4) Fabian Fröding (5) Duy Nguyen Ngoc (6) |
| Date | : | 10 2022 |
| Keyword | : | Class role-stereotypes,Machine learning classification,Program analysis,Design metrics,Software engineering Class role-stereotypes,Machine learning classification,Program analysis,Design metrics,Software engineering |
| Abstract | : | Role stereotypes are abstract characterisations of the responsibilities of the building blocks of software applications. The role a class plays within a software system reflects its design intention. Wirfs-Brock introduced the following six role stereotypes: Information Holder, which knows information, Structurer, which maintains object relationships, Service Provider, which offers computing services, Coordinator, which delegates tasks to others, Controller, which directs other’s actions, and Interfacer, which transforms information. Knowledge about class role stereotypes can help various software development and maintenance tasks, such as program understanding, program summarisation, and quality assurance. This paper presents an automated machine learning-based approach for classifying the role-stereotype of classes in Java projects. We analyse this approach’s performance against a manually labelled ground truth for three open source projects that contain 1,500+ Java classes altogether. The contributions of this paper include: (i) a machine learning (ML) approach to address the problem of automatically inferring role-stereotypes of classes in Object-Oriented Programming Languages, (ii) the manually labelled ground truth, (iii) an evaluation of the performance of the classifier, (iv) an evaluation of the generalisability of the approach, and (v) an illustration of new uses of role-stereotypes. The evaluation 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 different role stereotypes. In particular, its performance degrades when classifying rarer stereotypes. Among the 23 features that we study, features related to the classes’ collaboration characteristics and complexity stand out as the best discriminants of role stereotypes. |
| Group of Knowledge | : | Ilmu Komputer |
| Original Language | : | English |
| Level | : | Internasional |
| Status | : |
Published
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