Algorithms Comparison for Non-Requirements Classification using the Semantic Feature of Software Requirement Statements

Abstract

Noise in a Software Requirements Specification (SRS) is an irrelevant requirements statement or a non-requirements statement. This can be confusing to the reader and can have negative repercussions in later stages of software development. This study proposes a classification model to detect the second type of noise, the non-requirements statement. The classification model that is built is based on the semantic features of the non-requirements statement. This research also compares the five best-supervised machine learning methods to date, which are support vector machine (SVM), naive Bayes (NB), random forest (RF), k-nearest neighbor (kNN), and Decision Tree. This comparison aimed to determine which method can produce the best non-requirements classification, model. The comparison shows that the best model is produced by the SVM method with an average accuracy of 0.96. The most significant features in this non-requirement classification model are the requirements statement or non-requirements, id statement, normalized mean value, standard deviation value, similarity variant value, standard deviation normalization value, maximum normalized value, similarity variant normalization value, value Bad NN, mean value, number of sentences, bad VB score, and project id.