A general noise-reduction framework for fault localization of Java programs

Abstract

A B S T R A C TContext: Existing fault-localization techniques combine various program features and similarity coefficients with the aim of precisely assessing the similarities among the dynamic spectra of these program features to predict the locations of faults. Many such techniques estimate the probability of a particular program feature causing the observed failures. They often ignore the noise introduced by other features on the same set of executions that may lead to the observed failures. It is unclear to what extent such noise can be alleviated.Objective: This paper aims to develop a framework that reduces the noise in fault-failure correlation measurements. Method:We develop a fault-localization framework that uses chains of key basic blocks as program features and a noise-reduction methodology to improve on the similarity coefficients of fault-localization techniques. We evaluate our framework on five base techniques using five real-life medium-scaled programs in different application domains. We also conduct a case study on subjects with multiple faults.Results: The experimental result shows that the synthesized techniques are more effective than their base techniques by almost 10%. Moreover, their runtime overhead factors to collect the required feature values are practical. The case study also shows that the synthesized techniques work well on subjects with multiple faults. Conclusion:We conclude that the proposed framework has a significant and positive effect on improving the effectiveness of the corresponding base techniques.

Learning to Combine Multiple Ranking Metrics for Fault Localization

“…We use 25 existing ranking metrics [1], [18], [33], [31] in our ranking model. Table I lists the details of these 25 metrics.…”

“…Existing work by Jones et al [12], Abreu et al [2], and Naish et al [18] investigates fault localization with statements in C programs while Xu et al [31] addresses the basic blocks in Java programs. In our work, we follow Steimann et al [24], [25] use methods as the program entities.…”

“…Spectrum-based fault localization (also called coverage-based fault localization) is a class of fault localization approaches, which leverages the execution traces of test cases to predict the likelihood -called suspiciousness scores -of source code locations to be faulty. In spectrumbased fault localization, a faulty program is instrumented for collecting the running traces; then a ranking metric is applied to compute suspiciousness scores for program entities (such as methods [25], [24], basic blocks [31], and statements [12], [2]). The most suspicious entities are given to a developer for further analysis [21].…”

Comparative modelling and verification of Pthreads and Dthreads

“…The demands for multithreaded programs increase rapidly with the development of multiprocessors. However, the consequent concurrency issues, for example, deadlocks,) data races,( and race conditions, make concurrent programming more challenging. Deadlocks are often due to improper request/release of resources between multiple threads.…”

“…Deadlock detections can be conducted via static or dynamic techniques. Static techniques use static analysis tools to analyse the codes to find out the locations where deadlocks may occur. However, they usually require high computation cost and may not be suitable for large‐scale code.…”