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Automating Feature Model maintainability evaluation using machine learning techniques
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AI
DOI：10.1016/j.jss.2022.111539.png)
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Abstract
En 中文
Context: Software Product Lines (SPL) are generally specified using a Feature Model (FM), an artifact designed in the early stages of the SPL development life cycle. This artifact can quickly become too complex, which makes it challenging to maintain an SPL. Therefore, it is essential to evaluate the artifact's maintainability continuously. The literature brings some approaches that evaluate FM maintainability through the aggregation of maintainability measures. Machine Learning (ML) models can be used to create these approaches.Objective: This work proposes white-box ML models intending to classify the FM maintainability based on 15 measures.Methods: To build the models, we performed the following steps: (i) we compared two approaches to evaluate the FM maintainability through a human-based oracle of FM maintainability classifications; (ii) we used the best approach to pre-classify the ML training dataset; (iii) we generated three ML models and compared them against classification accuracy, precision, recall, F1 and AUC-ROC; and, (iv) we used the best model to create a mechanism capable of providing improvement indicators to domain engineers.Results: The best model used the decision tree algorithm that obtained accuracy, precision, and recall of 0.81, F1-Score of 0.79, and AUC-ROC of 0.91. Using this model, we could reduce the number of measures needed to evaluate the FM maintainability from 15 to 9 measures. Furthermore, we created a mechanism to suggest FM refactorings to improve the maintainability of this artifact. FM maintainability evaluation and the refactoring suggestion mechanism were automated in the DyMMer tool.Conclusion: We conclude this work by presenting a way to combine FM maintainability assessment with FM refactorings. The results of this work provide to domain engineers inputs that will allow them to carry out a continuous improvement of an SPL.(c) 2022 Elsevier Inc. All rights reserved.
Keywords:
Quality Assessment
Software Product Lines
Feature Model
Machine Learning
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