Automatic approval prediction for software enhancement requests

Abstract

Software applications often receive a large number of enhancement requests that suggest developers to fulfill additional functions. Such requests are usually checked manually by the developers, which is time consuming and tedious. Consequently, an approach that can automatically predict whether a new enhancement report will be approved is beneficial for both the developers and enhancement suggesters. With the approach, according to their available time, the developers can rank the reports and thus limit the number of reports to evaluate from large collection of low quality enhancement requests that are unlikely to be approved. The approach can help developers respond to the useful requests more quickly. To this end, we propose a multinomial naive Bayes based approach to automatically predict whether a new enhancement report is likely to be approved or rejected. We acquire the enhancement reports of open-source software applications from Bugzilla for evaluation. Each report is preprocessed and modeled as a vector. Using these vectors with their corresponding approval status, we train a Bayes based classifier. The trained classifier predicts approval or rejection of the new enhancement reports. We apply different machine learning and neural network algorithms, and it turns out that the multinomial naive Bayes classifier yields the highest accuracy with the given dataset. The proposed approach is evaluated with 40,000 enhancement reports from 35 open source applications. The results of tenfold cross validation suggest that the average accuracy is up to 89.25%.