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To ensure an acceptable level of quality and reliability of a typical software product, it is desirable to test every possible combination of input data under various configurations. However, due to the combinatorial explosion problem, exhaustive testing is practically impossible. Resource constraints, cost factors, and strict time-to-market deadlines are some of the main factors that inhibit such a consideration. Earlier research has suggested that a sampling strategy (i.e., one that is based on a t-way parameter interaction) can be effective. As a result, many helpful t-way sampling strategies have been developed and can be found in the literature. Several advances have been achieved in the last 15 years, which have, in particular, served to facilitate the test planning process by systematically minimizing the test size required (based on certain t-way parameter interactions). Despite this significant progress, the integration and automation of strategies (from planning process to execution) are still lacking. Additionally, strategizing to sample (and construct) a minimum test set from the exhaustive test space is an NP-complete problem; that is, it is often unlikely that an efficient strategy exists that could regularly generate an optimal test set. Motivated by these challenges, this paper discusses the design, implementation, and validation of an efficient strategy for t-way testing, the GTWay strategy. The main contribution of GTWay is the integration of t-way test data generation with automated (concurrent) execution as part of its tool implementation. Unlike most previous methods, GTWay addresses the generation of test data for a high coverage strength (t > 6).

To ensure an acceptable level of quality and reliability of a typical software product, it is desirable to test every possible combination of input data under various configurations. However, due to the combinatorial explosion problem, exhaustive testing is practically impossible. Resource constraints, cost factors, and strict time-to-market deadlines are some of the main factors that inhibit such a consideration. Earlier research has suggested that a sampling strategy (i.e., one that is based on a t-way parameter interaction) can be effective. As a result, many helpful t-way sampling strategies have been developed and can be found in the literature. Several advances have been achieved in the last 15 years, which have, in particular, served to facilitate the test planning process by systematically minimizing the test size required (based on certain t-way parameter interactions). Despite this significant progress, the integration and automation of strategies (from planning process to execution) are still lacking. Additionally, strategizing to sample (and construct) a minimum test set from the exhaustive test space is an NP-complete problem; that is, it is often unlikely that an efficient strategy exists that could regularly generate an optimal test set. Motivated by these challenges, this paper discusses the design, implementation, and validation of an efficient strategy for t-way testing, the GTWay strategy. The main contribution of GTWay is the integration of t-way test data generation with automated (concurrent) execution as part of its tool implementation. Unlike most previous methods, GTWay addresses the generation of test data for a high coverage strength (t > 6).

Neural networks have been used in the medical field in various applications such as medical imaging processing and disease diagnostic technique. In this paper, we investigate the capability of two conventional neural networks as an intelligent diagnostic system. In particular, the radial basis function (RBF) and multilayered perceptron (MLP) neural networks were used to classify the type of cervical cancer in its early stage. The study is divided into two stages. In the first stage, we investigate the applicability of neural networks to classify cervical cells into normal and abnormal cells. In the second stage, we classify cervical cells abnormality into three classes based on The Bethesda Classification System; normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). Diagnosis obtained using RBF and MLP neural networks gave promising results. Nevertheless, classification of abnormal cells into LSIL and HSIL yielded unsatisfactory results. In order to address this problem, this study adopted two hybrid neural networks namely hybrid radial basis function (HRBF) and hybrid multilayered perceptron (HMLP) networks in order to improve the performances of conventional neural networks. The overall diagnostic performance was measured using accuracy, sensitivity, specificity, false negative and false positive analysis by comparing to the diagnoses made by pathologists. This study indicates that HMLP network produces better overall diagnostic performance than the MLP, RBF and HRBF networks.