Combinatorial testing has been shown to be a very effective strategy for software testing. After a failure is detected, the next task is to identify one or more faulty statements in the source code that have caused the failure. In this paper, we present a fault localization approach, called BEN, which produces a ranking of statements in terms of their likelihood of being faulty by leveraging the result of combinatorial testing. BEN consists of two major phases. In the first phase, BEN identifies a combination that is very likely to be failure-inducing. A combination is failure-inducing if it causes any test in which it appears to fail. In the second phase, BEN takes as input a failure-inducing combination identified in the first phase and produces a ranking of statements in terms of their likelihood to be faulty. We conducted an experiment in which our approach was applied to the Siemens suite and four real-world programs, flex, grep, gzip and sed, from Software Infrastructure Repository (SIR). The experimental results show that our approach can effectively and efficiently localize the faulty statements in these programs.

This paper presents a method to perform sparse representation based classification (SRC) in a more accurate and efficient way. In this method, training data is first mapped into different feature spaces and multiple dictionaries are built by utilizing a Fisher discriminative based method. These dictionaries can be considered as efficient representations of the data which are then used in a multimodal SRC framework to classify test samples. In comparison to the original SRC method where only one modality of training space is utilized, the proposed method classifies test samples in a more accurate and efficient way. Experimental results from two different face datasets show that the proposed multimodal method has higher recognition rate compared to single-modality SRC based methods. The accuracy of the proposed method is also compared to other multi-modality classifiers and the results confirm that higher recognition rates are achieved in comparison with other common classification algorithms.

Combinatorial testing has been shown to be a very effective testing strategy. After a failure is detected, the next task is to identify the fault that causes the failure. In this paper, we present an approach to fault localization that leverages the result of combinatorial testing. Our approach is based on a notion called failure-inducing combinations. A combination is failure-inducing if it causes any test in which it appears to fail. Given a failure-inducing combination, our approach derives a group of tests that are likely to exercise similar traces but produce different outcomes. These tests are then analyzed to locate the faults. We conducted an experiment in which our approach was applied to the Siemens suite as well as the grep program from the SIR repository that has 10068 lines of code. The experimental results show that our approach can effectively and efficiently localize the faults in these programs.

A t-way combinatorial test set is designed to detect failures that are triggered by combinations involving no more than t parameters. Assume that we have executed a t-way test set and some tests have failed. A natural question to ask is: What combinations have caused these failures? Identifying such combinations can facilitate the debugging effort, e.g., by reducing the scope of the code that needs to be inspected. In this paper, we present an approach to identifying failure-inducing combinations, i.e., combinations that have caused some tests to fail. Given a t-way test set, our approach first identifies and ranks a set of suspicious combinations, which are candidates that are likely to be failure-inducing combinations. Next, it generates a set of new tests, which can be executed to refine the ranking of suspicious combinations in the next iteration. This process can be repeated until a stopping condition is satisfied. We conducted an experiment in which our approach was applied to several benchmark programs. The experimental results show that our approach can effectively and efficiently identify failure-inducing combinations in these programs.