Classification Software Engineering Documents Based on Hybrid Model
AL-Rafidain Journal of Computer Sciences and Mathematics,
2018, Volume 12, Issue 2, Pages 61-87
AbstractCare about automated documents classification has increased since the appearance of the digital documents and the wide diffusion of Internet. In the 1990's, the computer performance has greatly improved and has led to the methods of machine learning to establish automated classifiers. These methods have achieved good speed and classification's accuracy and researchers still investigate in this field to accomplish more accuracy and less time. Artificial immunologic systems have shown high performance in such as data clustering and anomaly detection which can be ascribed to the nature of the immunologic system in protecting the body.
Some of the present methods and ways used in the training process of the document classification are time consuming and others have less accuracy rate concerned with the classification of the related document as software engineering document classes. For these reasons, this research deals with the study of Natural Immune System and using the dynamic process of the Adaptive Immune System work by hybridization Negative Selection (NS) and Positive Selection (PS) techniques and to propose a hybrid model called the Hybrid Positive Negative Selection Model (HPNS) to classify Software Engineering documents as they comprise information related to developing the software systems, that makes it easy for the software engineer who works in maintenance.
HPNS has high classification's speed and accuracy besides easy and flexible use by designing interfaces that make it easy for the user to deal with the system. In order to improve the quality and the efficiency of HPNS method, it was compared to one of the best and well-known methods of classification referred to as, Naive Bayes(NB). After conducting several experiments on a various group of software engineering documents, evaluations results have shown that the accuracy of the Adaptive immunologic method (HPNS) has reached (HPNS) (95%), whereas Naïve classification method has reached (90 %) with training and classification speed that doesn’t exceed one minute. This shows the feasibility of using the algorithms of AIS systems in the field of information recovery and documents classification. This system was built and programmed in Java language and was implemented under an operating system environment Microsoft Windows7.
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