Classification of application reviews into software maintenance tasks using data mining techniques

Mobile application reviews are considered a rich source of information for software engineers to provide a general understanding of user requirements and technical feedback to avoid main programming issues. Previous researches have used traditional data mining techniques to classify user reviews into several software maintenance tasks. In this paper, we aim to use associative classification (AC) algorithms to investigate the performance of different classifiers to classify reviews into several software maintenance tasks. Also, we proposed a new AC approach for review mining (ACRM). Review classification needs preprocessing steps to apply natural language preprocessing and text analysis. Also, we studied the influence of two feature selection techniques (information gain and chi-square) on classifiers. Association rules give a better understanding of users’ intent since they discover the hidden patterns in words and features that are related to one of the maintenance tasks, and present it as class association rules (CARs). For testing the classifiers, we used two datasets that classify reviews into four different maintenance tasks. Results show that the highest accuracy was achieved by AC algorithms for both datasets. ACRM has the highest precision, recall, F-score, and accuracy. Feature selection helps improving the classifiers’ performance significantly.