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Features and Supervised Machine Learning Based Method for Singleton Design Pattern Variants Detection 18

Features and Supervised Machine Learning Based Method for Si...

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18th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

作者： Nacef, Abir Bahroun, Sahbi Khalfallah, Adel Ben Ahmed, Samir Tunis El Manar Univ Comp Lab Ind Syst Fac Math Phys & Nat Sci Tunis FST Tunis Tunisia Tunis El Manar Univ Higher Inst Comp Sci ISI Limt Lab Tunis Tunisia

ISBN: (纸本)9789897586477

Design patterns codify standard solutions to common problems in software design and architecture. Given their importance in improving software quality and facilitating code reuse, many types of research are proposed on their automatic detection. In this paper, we focus on singleton pattern recovery by proposing a method that can identify orthodox implementations and non-standard variants. The recovery process is based on specific data created using a set of relevant features. These features are specific information defining each variant which is extracted from the Java program by syntactical and semantic analysis. We are based on the singleton analysis and different proposed features in ou previous work (Nacef et al., 2022) to create structured data. This data contains a combination of feature values defining each singleton variant to train a supervised Machine Learning (ML) algorithm. The goal is not limited to detecting the singleton pattern but also the specification of the implemented variant as so as the incoherent structure that inhib the pattern intent. We use different ML algorithms to create the Singleton Detector (SD) and compare their performance. The empirical results demonstrate that our method based on features and supervised ML, can identify any singleton implementation with the specific variant's name achieving 99% of precision, and recall. We have compared the proposed approach to similar studies namely DPDf and GEML. The results show that the SD outperforms the state-of-the-art approaches by more than 20% on evaluated data constructed from different repositories;PMART, DPB and DPDf corpus in terms of precision.

关键词： Design Patterns Singleton Variant rnn-lstm classifier Machine Learning SD classifier

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Defining and Extracting Singleton Design Pattern Information from Object-Oriented Software Program 14th

Defining and Extracting Singleton Design Pattern Information...

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14th International Conference on Computational Collective Intelligence (ICCCI)

作者： Nacef, Abir Khalfallah, Adel Bahroun, Sahbi Ben Ahmed, Samir Tunis El Manar Univ Lab Informat Syst Ind Fac Math Phys & Nat Sci Tunis FST Tunis Tunisia Tunis El Manar Univ Inst Super Informat ISI Lab Limt Tunis Tunisia

ISBN: (纸本)9783031162107;9783031162091

In software engineering (SE), improving the quality of code and design by relying on pre-established restructuring (refactoring), such as detection and injection of a design pattern are still challenging problems. In this article, we focus on the Singleton design pattern, in which we indicate its variants implementation and define 33 features that can identify this pattern in its standard and non-standard form. Significant information can be extracted by applying the structural and semantic analysis of the source code. So use this information;it becomes easier to identify a pattern and inject it. We created specific data using 20,000 code snippets. This data is used to train deep learning models called rnn-lstm classifiers to extract information from object-oriented software systems. The empirical result proves that our proposed lstm-rnn classifier can successfully extract proposed information with excellent results in terms of prediction recall and F1-score.

关键词： Singleton design pattern rnn-lstm classifier Object-oriented program analyses

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