Treffer: An Improved Hybrid Deep Learning Approach for Security Requirements Classification.

As the trend to use the latest machine learning models to automate requirements engineering processes continues, security requirements classification is tuning into the most researched field in the software engineering community. Previous literature studies have proposed numerous models for the classification of security requirements. However, adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning algorithms. Moreover, most of the researchers focus only on the classification of requirements with security keywords. They did not consider other nonfunctional requirements (NFR) directly or indirectly related to security. This has been identified as a significant research gap in security requirements engineering. The major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security requirements. We use PROMISE_exp and DOSSPRE, the two most commonly used datasets in the software engineering community. The proposed methodology consists of two steps. In the first step, we analyze all the nonfunctional requirements and their relation with security requirements. We found 10 NFRs that have a strong relationship with security requirements. In the second step, we categorize those NFRs in the security requirements category. Our proposed methodology is a hybrid model based on the Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost) models. Moreover, we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security categories. The performance is evaluated using four metrics: recall, precision, accuracy, and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3% and 85.3% accuracy, respectively. The proposed study shows an enhancement in metrics values compared to the previous literature studies. This is a proof of concept for systematizing the evaluation of security recognition in software systems during the early phases of software development. [ABSTRACT FROM AUTHOR]

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