Abstract
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.
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Nacef, A., Khalfallah, A., Bahroun, S., Ben Ahmed, S. (2022). Defining and Extracting Singleton Design Pattern Information from Object-Oriented Software Program. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_58
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