Probabilistic detection of GoF design patterns
Pages 1654 - 1682
Abstract
Abstract
Detecting design patterns from source code of software systems can help to understand the structure and the behavior of the software systems. The better understanding of software systems is helpful in reengineering and refactoring. As software progression, refactoring has become more valuable. One way to reduce the refactoring costs is to detect design patterns. The key criteria for accurately detecting design patterns is signatures. Achieving fine signatures is not an easy forward task. Instead of improving signatures, more accurate detection can be achieved by having probabilistic viewpoints. Since each of the design patterns has variants or may be implemented differently, having a probabilistic approach in detection can increase coverage as well as help in software refactoring. In this study, the main purpose is to identify the design patterns in source code with a non-crisp approach and measuring the possibility of the presence of the design patterns in the source code. Considering main body of design patterns and their corresponding signatures, design patterns are represented as appropriate features. We try to get features from design pattern signatures that do not change in the face of variations that occur during implementation. Then, through these features, the probability of presence of the roles forming the design patterns is determined, using neural network and regression analysis. After this step, using probabilistic graphical models the probability of presenting design patterns in source code is measured. The results of the proposed method show the similarity of each code to the design patterns in the range between 0 and 1. The results of other valid methods are a subset of the results of proposed method. Results that are 50% to 100% similar to the design patterns are presented in the evaluation section.
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Index Terms
- Probabilistic detection of GoF design patterns
Index terms have been assigned to the content through auto-classification.
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Kluwer Academic Publishers
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Publication History
Published: 01 August 2022
Accepted: 11 July 2022
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