With the growing complexity and scale of software, detecting and repairing errant behaviors at an early stage are critical to reduce the cost of software development. In the practice of fault localization, a typical process usually includes three steps: execution of input domain test cases, construction of model domain test vectors and suspiciousness evaluation. The effectiveness of model domain test vectors is significant for locating the faulty code. However, test vectors with failing labels usually account for a small portion, which inevitably degrades the effectiveness of fault localization. In this paper, we propose a data augmentation method PVaug by using fault propagation context and variational autoencoder (VAE). Our empirical results on 14 programs illustrate that PVaug has promoted the effectiveness of fault localization.

To reduce the inaccuracy caused by inappropriate time window, we propose two probabilistic fault localization schemes based on the idea of "extending time window." The global window extension algorithm (GWE) uses a window extension strategy for all candidate faults, while the on-demand window extension algorithm (OWE) uses the extended window only for a small set of faults when necessary. Both algorithms can increase the metric values of actual faults and thus improve the accuracy of fault localization. Simulation results show that both schemes perform better than existing algorithms. Furthermore, OWE performs better than GWE at the cost of a bit more computing time.