Supervised Representation Learning Approach for Cross-project Aging-Related Bug Prediction

Software aging, which is caused by Aging-Related Bugs (ARBs), tends to occur in long-running systems and may lead to performance degradation and increasing failure rate during software execution. ARB prediction can help developers discover and remove ARBs, thus alleviating the impact of software aging. However, ARB-prone files occupy a small percentage of all the analyzed files. It is usually difficult to gather sufficient ARB data within a project. To overcome the limited availability of training data, several researchers have recently developed cross-project models for ARB prediction. A key point for cross-project models is to learn a good representation for instances in different projects. Nevertheless, most of the previous approaches neither consider the reconstruction property of new representation nor encode source samples' label information in learning representation. To address these shortcomings, we propose a Supervised Representation Learning Approach (SRLA), which is based on double encoding-layer autoencoder, to perform cross-project ARB prediction. Moreover, we present a transfer cross-validation framework to select the hyper-parameters of cross-project models. Experiments on three large open-source projects demonstrate the effectiveness and superiority of our approach compared with the state-of-the-art approach TLAP.