Multi-Class Classification of ISO/IEC 25010 Software Quality Metrics Using User Feedback

Software quality indicates how effective and efficient a software is. Various standards need to be used to evaluate software quality. One of the most important internationally accepted standards for measuring software quality is the ISO/IEC 25010 software quality standard. With this standard, the quality of a software product is evaluated with eight different metrics. These are functional suitability, performance, compatibility, usability, reliability, security, maintainability and portability metrics. In this study, we tried to determine the relationship between user feedback and the metrics of the ISO/IEC 25010 software quality standard. Machine learning (ML) and natural language processing (NLP) techniques were used to classify user comments. After the data preprocessing phase, vectors of user comments were extracted with the Tf-Idf method for NLP. As a machine learning method, classification was made using five different models: Extra Trees Classifier (ETC), Gaussian Process Classifier (GPC), MLP Classifier (MLPC), Bernoulli Naive Bayes Classifier (BNBC) and Support Vector Classifier (SVC). Our goal is to show how quality metrics can be classified into multiple classes using user notifications. The data set used has an unbalanced structure, containing 1681 user comments classified by software experts. Synthetic Minority Oversampling Technique (SMOTE) was used to address the imbalance in the dataset. The results were compared by applying the same classification models to unbalanced and balanced data sets. According to the results obtained, the best classification model is the Extra Trees Classifier model, which provides the highest accuracy rate of 87% according to the SMOTE applied data set. The results show that ML and NLP methods can be used effectively in the classification process of software quality metrics.