Prediction of bug‐fixing time based on distinguishable sequences fusion in open source software

Abstract

AbstractGenerally, open source software (OSS) has a longer bug‐fixing time. If the bug‐fixing time can be predicted accurately as early as possible, it will be beneficial to the efficiency of bug fixing. Traditional bug‐fixing time prediction models are usually based on static features of bug report. It is difficult to go into service due to inappropriate feature extraction of data and low prediction accuracy of models. The HMM prediction model can predict the bug‐fixing time accurately according to earlier fixing activities. However, this method of temporal sequence feature selection results in a large number of inconsistent samples, and the HMM prediction model can only capture the adjacent activity behavior information of one sequence, and hence, it will reduce the performance of bug‐fixing time prediction. By incorporating the activity information and time information of bug activity transfer, the proportion of inconsistent samples is reduced significantly. In this paper, a double‐sequence input LSTM model (LSTM‐DA) is designed to capture both sequences interaction features and long‐distance‐dependent features. The results of the experiments show that the proposed model can improve the F‐measure and accuracy indicators by about 10% compared with the HMM model in all dimensions, which demonstrates the effectiveness of our method.