Laleh Shikh Gholamhossein Ghandehari

Learn More
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(More)
The input space of a system must be modeled before combinatorial testing can be applied to this system. The effectiveness of combinatorial testing to a large extent depends on the quality of the input space model. In this paper we introduce an input space modeling methodology for combinatorial testing. The main idea is to consider the process of input space(More)
Combinatorial testing has attracted a lot of attention from both industry and academia. A number of reports suggest that combinatorial testing can be effective for practical applications. However, there are few systematic, controlled studies on the effectiveness of combinatorial testing. In particular, input parameter modeling is a key step in the(More)
Some conflicting results have been reported on the comparison between t-way combinatorial testing and random testing. In this paper, we report a new study that applies t-way and random testing to the Siemens suite. In particular, we investigate the stability of the two techniques. We measure both code coverage and fault detection effectiveness. Each program(More)
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(More)
We propose an efficient and accurate classification method based on Sparse Representation based Classification (SRC) for face recognition. In this approach, instead of using all or a subset, we use cluster centers of training samples to build SRC models. Considering the variability and redundancy of training samples, each class will be represented by a(More)
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(More)
We present a combinatorial testing-based fault localization tool called BEN. BEN takes as input three types of information, including the subject program, the source code, an input parameter model, and a combinatorial test set created based on the input parameter model. It is assumed that the combinatorial test set has already been executed, and thus the(More)