Automated program repair recently received considerable attentions, and many techniques on this research area have been proposed. Among them, two genetic-programming-based techniques, GenProg and Par, have shown the promising results. In particular, GenProg has been used as the baseline technique to check the repair effectiveness of new techniques in much literature. Although GenProg and Par have shown their strong ability of fixing real-life bugs in nontrivial programs, to what extent GenProg and Par can benefit from genetic programming, used by them to guide the patch search process, is still unknown.

Despite the availability of garbage collectors, programmers must manually manage non-memory finite system resources such as file descriptors. Resource leaks can gradually consume all available resources and cause programs to raise resource exhaustion exceptions. However, programmers commonly provide no effective recovery approach for resource exhaustion exceptions, which often causes programs to halt without completing their tasks. In this paper, we propose to automatically recover programs from resource exhaustion exceptions caused by resource leaks. We transform programs to catch resource exhaustion exceptions, collect leaked resources, and then retry the failure code. A resource collector is designed to identify leaked resources and safely release them. We implement our approach for Java programs. Experimental results show that our approach can successfully handle resource exhaustion exceptions caused by reported resource leaks and allow programs to complete their tasks with an average execution time increase of 2.52% and negligible bytecode size increase.

Researchers have proposed many spectra-based fault localization (SBFL) techniques in the past decades. Existing studies evaluate the effectiveness of these techniques from the viewpoint of developers, and have drawn some important conclusions through either empirical study or theoretical analysis. In this paper, we present the first study on the effectiveness of SBFL techniques from the viewpoint of fully automated debugging including the program repair of automation, for which the activity of automated fault localization is necessary. We assess the accuracy of fault localization according to the repair effectiveness in the automated repair process guided by the localization technique. Our experiment on 14 popular SBFL techniques with 11 subject programs shipping with real-life field failures presents the evidence that some conclusions drawn in prior studies do not hold in our experiment. Based on experimental results, we suggest that Jaccard should be used with high priority before some more effective SBFL techniques specially proposed for automated program repair occur in the future.

Recent techniques for fault localization statistically analyze coverage information of a set of test runs to measure the correlations between program entities and program failures. However, coverage information cannot identify those program entities whose execution affects the output, which weakens the aforementioned correlations. Thus, this paper proposes a novel statistical fault localization approach to address this problem. Our statistical approach utilizes program slices of a set of test runs to capture the influence of a program entity's execution on the output, and uses statistical analysis to measure the suspiciousness of program entities to be faulty. In addition, this paper presents a new slicing approach called approximate dynamic backward slice to balance the size and accuracy of a slice, and applies this slice to our statistical approach. The experimental results on two standard benchmarks show that our statistical approach significantly outperforms eight representative fault localization techniques.

As an emerging paradigm for architecting, service-oriented computing plays a more and more important role in information technology. To ensure that web services are working according with the expectation, the research in service behavior about interactions between services is crucial to guarantee no deviation from specification. To detect these deviations, we propose a method to mine and check web service behavior. The method attempts to apply process mining to interactions between services in order to narrow the gap between service consumers and providers. By observing executions of services, we present three levels of abstraction on service behavior: internal behavior, external behavior and workflow behavior. Then we outline the approach to extract behavioral model that provides specification to the activity of service checking. Finally the framework of service behavior checking is presented.