Automated identification and classification of usability aspect of stack overflow constraints

A collection of constraints on stack overflow can be used as material for evaluating the quality of software so that developers can improve the quality of the software utilizing text mining. the study aims to determine the usability aspects of software based on the classification of questions on stack overflow to ease the task of developers in evaluating the quality of the software. This research has several processes, namely: first, we do preprocess data, then data that has been preprocessed, is classified to get data that reflects the usability attribute, data that includes usability attributes will be calculated inverse document frequency and sorting to get the 20 highest term scores which are an aspect of the usability attribute. This study succeeded in getting 20 aspects of the usability of the results of the question extraction process using the classification process by comparing the five classification methods, namely: Naive Bayes, Support Vector Machine, Neural Networks, Logistic Regression, and Random Forest. The best accuracy results are obtained when using the Naive Bayes method with a value of 70 percent, a usability grade precision of 71 percent, and a recall value of 67%. In the Non-Usability class, the values of precision and recall are 70 percent and 74 percent, respectively.