Classifying issue reports according to feature descriptions in a user manual based on a deep learning model

Abstract

Issue reports are documents with which users report problems and state their opinions on a software system. Issue reports are useful for software maintenance, but managing them requires developers’ considerable manual effort. To reduce such effort, previous studies have mostly suggested methods for automatically classifying issue reports. However, most of those studies classify issue reports according to issue types, based only on whether the report is relevant to a bug, whether the report is duplicated, or whether the issue is functional or nonfunctional. In this paper, we intend to link issue reports and a user manual and so propose a deep learning model-based method that classifies issue reports according to software features that are described in the user manual in order to help developers relate issue reports to features to make changes to a software system. In order to classify issue reports according to the feature descriptions in a user manual, our method uses a deep learning technique with a word embedding technique. The key insight in our method is that the sections of a user manual that describe software features contain the words and sentences similar to those in issue reports. Based on the insight, we construct a classification model that learns the feature descriptions (i.e. sections) in a user manual and classifies issue reports according to the feature descriptions. We evaluate the proposed method by comparing its classification performance with that of the state-of-the-art method, TicketTagger. The experimental results show that the proposed method yields 10% ∼ 24% higher classification f1-score than that of TicketTagger. We also experiment with two deep learning models and four word embedding techniques and find out that the Convolution Neural Network model with FastText (or GloVe) yields the best performance. Our study shows the feasibility of classifying issue reports according to software features, which can be the basis for successive studies to classify issue reports into software features.