Identifying Crashing Fault Residence Based on Cross Project Model Analyzing the crash reports recorded upon software crashes is a critical activity for software quality assurance. Predicting whether or not the fault causing the crash (crashing fault for short) resides in the stack traces of crash reports can speedup the program debugging process and determine the priority of the debugging efforts. Previous work mostly collected label information from bugfixing logs, and extracted crash features from stack traces and source code to train classification models for the Identification of Crashing Fault Residence (ICFR) of newlysubmitted crashes. However, labeled data are not always fully available in real applications. Hence the classifier training is not always feasible. In this work, we make the first attempt to develop a cross project ICFR model to address the data scarcity problem. This is achieved by transferring the knowledge from external projects to the current project via utilizing a stateoftheart Balanced Distribution Adaptation (BDA) based transfer learning method. BDA not only combines both marginal distribution and conditional distribution across projects but also assigns adaptive weights to the two distributions for better adjusting specific cross project pair. The experiments on 7 software projects show that BDA is superior to 9 baseline methods in terms of 6 indicators overall.

Supervised Representation Learning Approach for CrossProject AgingRelated Bug Prediction Software aging, which is caused by AgingRelated Bugs (ARBs), tends to occur in longrunning 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, ARBprone 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 crossproject models for ARB prediction. A key point for crossproject 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 samplesu0027 label information in learning representation. To address these shortcomings, we propose a Supervised Representation Learning Approach (SRLA), which is based on double encodinglayer autoencoder, to perform crossproject ARB prediction. Moreover, we present a transfer crossvalidation framework to select the hyperparameters of crossproject models. Experiments on three large opensource projects demonstrate the effectiveness and superiority of our approach compared with the stateoftheart approach TLAP.