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

Nacef, Abir; Khalfallah, Adel; Bahroun, Sahbi; Ahmed, Samir Ben

doi:10.1007/978-3-031-16210-7_58

Cited by 2 publications

(10 citation statements)

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“…The recognition of instances of singleton patterns in source code is difficult, caused of the different implementations, which are also not formally defined. So that we depart from the singleton pattern analysis and their variants presented in [1].…”

Section: Reported Variants and Proposed Features For Singleton Design...mentioning

confidence: 94%

“…On the other hand, extracting semantic information from source code is still a difficult task due to the complexity and diverse representation of the code. Therefore, to solve this problem, we are based on the work proposed by Nacef et al in [1] to define singleton variants rules and create specific data to recover each variant from the source code. Nacef et al [1] have proposed a set of features for identifying singleton patterns based on a detailed analysis of the variant's structure and behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, to solve this problem, we are based on the work proposed by Nacef et al in [1] to define singleton variants rules and create specific data to recover each variant from the source code. Nacef et al [1] have proposed a set of features for identifying singleton patterns based on a detailed analysis of the variant's structure and behaviors. For analyzing the java program Nacef et al apply structural and semantic analysis by the use of the LSTM model to extract semantic information (features).…”

Section: Introductionmentioning

confidence: 99%

“…The LSTM model is trained by specifically structured data corresponding to each feature for a classification task. Since the source code is a special structured natural language, written by programmers [6], the created data in [1] contains snippets of code treated as plain text. Nacef et al exploit the efficiency of LSTM models in Computer Vision and Natural Language Processing (NLP) [7] evidenced by many recent works, to recover semantic information and elements dependencies from java programs without using any intermediate representation.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose a Feature-Based Singleton Design Pattern Detection approach that uses a set of features extracted from both structural and semantic analyses [1]. We create specific structured data based on the feature's combination values corresponding to each singleton variant to train a supervised ML model named SD.…”

Section: Introductionmentioning

confidence: 99%

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

Nacef

Bahroun

Khalfallah

et al. 2022

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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 structural and semantic analysis. We are based on the singleton analysis and different proposed features presented by Nacef et al. in [1] 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 incorrect structure that is incoherent with the pattern intent. We use different ML algorithms to create the Singleton Detector (SD) to compare their performance. The empirical results demonstrate that our method for automatically training the (SD) 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.

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Section: Reported Variants and Proposed Features For Singleton Design...mentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%