Using Knowledge Transfer and Rough Set to Predict the Severity of Android Test Reports via Text Mining

Abstract

Abstract:Crowdsourcing is an appealing and economic solution to software application testing because of its ability to reach a large international audience. Meanwhile, crowdsourced testing could have brought a lot of bug reports. Thus, in crowdsourced software testing, the inspection of a large number of test reports is an enormous but essential software maintenance task. Therefore, automatic prediction of the severity of crowdsourced test reports is important because of their high numbers and large proportion of noise. Most existing approaches to this problem utilize supervised machine learning techniques, which often require users to manually label a large number of training data. However, Android test reports are not labeled with their severity level, and manual labeling is time-consuming and labor-intensive. To address the above problems, we propose a Knowledge Transfer Classification (KTC) approach based on text mining and machine learning methods to predict the severity of test reports. Our approach obtains training data from bug repositories and uses knowledge transfer to predict the severity of Android test reports. In addition, our approach uses an Importance Degree Reduction (IDR) strategy based on rough set to extract characteristic keywords to obtain more accurate reduction results. The results of several experiments indicate that our approach is beneficial for predicting the severity of android test reports.

A systemic framework for crowdsourced test report quality assessment

“…Under the motivation of reducing the inspection cost, Jiang et al developed a new framework to partition test reports into clusters in which test reports in one cluster detail the same bug (Jiang et al 2018). In addition, to help developers determine the severity lever of test reports, Guo et al proposed a knowledge transfer classification technique which leverages similar information contained in bug reports (Guo et al 2017). Different from the above studies, this study aims to evaluate the quality of test reports to help developers select high-quality test reports for inspection, thus accelerating inspection efficiency.…”

Machine Learning Algorithm’s Measurement and Analytical Visualization of User’s Reviews for Google Play Store

“…The conclusion is that current store ratings are not dynamic enough to capture changing user satisfaction levels. This resilience is a significant problem that can discourage developers from improving app quality [33].…”

Predicting the Priority of Bug Reports Using Classification Algorithms

“…Concluded that naïve byes multinomial gave good accuracy over other three. In [18], authors used Naïve Bayes classification for predicting the severity of android bug reports. Eclipse and Mozilla bug datasets are taken from Bugzilla bug repositories for training and for testing android bug reports are taken from the Android bug tracker system which are unlabeled.…”