String validation routines have been widely used in many real-world applications, such as email validation and postcode validation. String test cases are adopted to test these validation routines, to identify potential defects and security risks. Random Testing (RT) is a well-known testing approach to randomly generate string test cases from the input domain (i.e., the set of all possible test inputs), which is simple to implement at a low cost. However, its testing effectiveness may be unsatisfactory for string validation routines. The main reason for this is that RT may have a high probability to generate invalid rather than valid string test cases, due to its randomness property. This research proposes a new RT approach based on the output types (i.e., valid and invalid strings) for string validation routines, namely Output-type-guided Random Testing (RT-O), which attempts to randomly generate both valid and invalid string test cases with a certain probability. This research performed an empirical study involving several real-world string validation routines collected from ten Java open-source projects, to investigate and compare testing performances of RT-O against the previous two widely-used RT methods. The results show that the generated string test cases by RT-O outperform test cases generated by other RT methods.

Abstract Test Cases (ATCs) have been widely used in practical scenarios such as combinatorial testing. When the set of ATCs is constructed, Test Case Prioritization (TCP) is applied to improve the testing quality. Among the many TCP approaches for ATCs, one intuitive and well-studied approach is Interaction Coverage Based Prioritization (ICBP), which greedily identifies one element as the next ATC such that it covers the largest number of uncovered -wise parameter-value combinations. When a -wise value combination, vc, has been covered by the already-selected ATCs, it is generally believed that it may have no further role to play in the ICBP process. However, each already-covered vc may provide some potential contributions for the interaction coverage of the strength higher than . Motivated by this, we propose a new TCP approach to prioritize ATCs, Weighted Interaction Coverage Based Prioritization (WICBP). WICBP attempts to assign a weight to each -wise value combination that has already been covered. The experimental results show that when using the same prioritization strength, WICBP is more effective than ICBP but maintains comparable testing efficiency. In addition, WICBP with lower prioritization strengths achieves surprisingly competitive (even better) performance compared with ICBP with higher prioritization strengths.

Software quality evaluation is a challenging task in software engineering. A new group decision-making evaluation model is presented in this work. The new model is based on the Vlsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) technique, in which a group regret measurement and a group satisfaction measurement are provided to increase the number of reference criteria in the decision-making process. We choose the median to represent the center of the data. Based on this, an entropy-based weighting method is proposed and used to determine the weights of decision makers. A new normalized projection is explored to measure the closeness between two evaluation matrices in a Pythagorean fuzzy setting. Several experimental analyses demonstrate that the entropy-based weighting method developed in this study is superior to traditional weighting methods. The median-based data center provides support for stable decision outcomes. Four dynamic experiments are reported on in this paper: The first one shows that the decision results remain stable throughout the entire experimental range; the second one demonstrates that the proposed normalized projection measure outperforms traditional projection measure; the third one demonstrates that the newly developed VIKOR method outperforms the traditional VIKOR method; and the last one identifies the optimal range for the three parameters of the proposed comprehensive VIKOR model.

Although Convolutional Neural Networks (CNNs) have achieved remarkable success in image classification, most CNNs use image datasets in the Red-Green-Blue (RGB) color space (one of the most commonly used color spaces). The existing literature regarding the influence of color space use on the performance of CNNs is limited. This paper explores the impact of different color spaces on image classification using CNNs. We compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets, each converted to nine color spaces. We find that color space selection can significantly affect classification accuracy, and that some classes are more sensitive to color space changes than others. Different color spaces may have different expression abilities for different image features, such as brightness, saturation, hue, etc. To leverage the complementary information from different color spaces, we propose a pseudo-Siamese network that fuses two color spaces without modifying the network architecture. Our experiments show that our proposed model can outperform the single-color-space models on most datasets. We also find that our method is simple, flexible, and compatible with any CNN and image dataset.