A Kidney Algorithm for Pairwise Test Suite Generation

Pairwise testing can greatly minimize the cost of software testing and also increase the ability of fault detection. Nevertheless, generating the most optimal test suite is an NP-complete problem and still an open area for research. The test case generation is the most active area of the pairwise testing research. Metaheuristic algorithms have been broadly used for solving difficult optimization problems as well as proving their effectiveness to get most optimal solutions. Kidney algorithm (KA) is a recent metaheuristic algorithm. This study introduces a new pairwise strategy by adapting KA; which is the first time to adapt KA in generating the test suite. The proposed strategy is called Pairwise Kidney Strategy (PKS). This study also highlights the PKS design; in addition, compare its performance with other reported strategies in the literature in terms of test suite size. Experiment results show that PKS has very competitive results as compared with other strategies

Exploiting an Elitist Barnacles Mating Optimizer implementation for substitution box optimization

Barnacles Mating Optimizer (BMO) is a new metaheuristic algorithm that suffers from slow convergence and poor efficiency due to its limited capability in exploiting the search space and exploring new promising regions. Addressing these shortcomings, this paper introduces Elitist Barnacles Mating Optimizer (eBMO). Unlike BMO, eBMO exploits the elite exponential probability (Pelite) to decide whether to intensify search process via swap operator or to diversify search by randomly exploring new regions. Furthermore, eBMO uses Chebyshev map instead of random numbers to generate quality S-boxes. Experimental results of eBMO on the generation of 8 × 8 substitution-box are competitive against other existing works

ABC Algorithm for Combinatorial Testing Problem

Computer software is in high demand everywhere in the world. The high dependence on software makes software requirements more complicated. As a result, software testing tasks get costlier and challenging due to a large number of test cases, coupled with the vast number of the system requirements. This challenge presents the need for reduction of the system redundant test cases. A combinatorial testing approach gives an intended result from the optimization of the system test cases. Hence, this study implements a combinatorial testing strategy called Artificial Bee Colony Test Generation (ABC-TG) that helps to get rid of some of the current combinatorial testing strategies. Results obtained from the ABC-TG were benchmarked with the results obtained from existing strategies in order to determine the efficiency of the ABC-TG. Finally, ABC-TG shows the efficiency and effectiveness in terms of generating optimum test cases size of some of the case studies and a comparable result with the existing combinatorial testing strategies

Glycation marker glucosepane increases with the progression of osteoarthritis and correlates with morphological and functional changes of cartilage in vivo

Background: Changes of serum concentrations of glycated, oxidized, and nitrated amino acids and hydroxyproline and anticyclic citrullinated peptide antibody status combined by machine learning techniques in algorithms have recently been found to provide improved diagnosis and typing of early-stage arthritis of the knee, including osteoarthritis (OA), in patients. The association of glycated, oxidized, and nitrated amino acids released from the joint with development and progression of knee OA is unknown. We studied this in an OA animal model as well as interleukin-1β-activated human chondrocytes in vitro and translated key findings to patients with OA. Methods: Sixty male 3-week-old Dunkin-Hartley guinea pigs were studied. Separate groups of 12 animals were killed at age 4, 12, 20, 28 and 36 weeks, and histological severity of knee OA was evaluated, and cartilage rheological properties were assessed. Human chondrocytes cultured in multilayers were treated for 10 days with interleukin-1β. Human patients with early and advanced OA and healthy controls were recruited, blood samples were collected, and serum or plasma was prepared. Serum, plasma, and culture medium were analyzed for glycated, oxidized, and nitrated amino acids. Results: Severity of OA increased progressively in guinea pigs with age. Glycated, oxidized, and nitrated amino acids were increased markedly at week 36, with glucosepane and dityrosine increasing progressively from weeks 20 and 28, respectively. Glucosepane correlated positively with OA histological severity (r = 0.58, p < 0.0001) and instantaneous modulus (r = 0.52–0.56; p < 0.0001), oxidation free adducts correlated positively with OA severity (p < 0.0009–0.0062), and hydroxyproline correlated positively with cartilage thickness (p < 0.0003–0.003). Interleukin-1β increased the release of glycated and nitrated amino acids from chondrocytes in vitro. In clinical translation, plasma glucosepane was increased 38% in early-stage OA (p < 0.05) and sixfold in patients with advanced OA (p < 0.001) compared with healthy controls. Conclusions: These studies further advance the prospective role of glycated, oxidized, and nitrated amino acids as serum biomarkers in diagnostic algorithms for early-stage detection of OA and other arthritic disease. Plasma glucosepane, reported here for the first time to our knowledge, may improve early-stage diagnosis and progression of clinical OA

Evaluating Bees Algorithm for Sequence-based T-way Testing Test Data Generation

T-way testing is a testing technique that is used to detect faults due to parameter interactions or software configurations. In order to perform t-way testing, software testers need to prepare the test data. For a system with many configuration parameters, the test data could lead to combinatorial explosion problem, since all possible parameter combinations need to be considered. Therefore, test data generation for t-way testing need to be optimized. Many t-way test data generation strategies have been proposed in the literature to generate optimized t-way test data. However, very few strategies have been proposed for sequence-based t-way. This paper presents statistical analysis on the performance of Bees Algorithm against the other sequence t-way strategies, in order to generate test cases

ABC Algorithm for Combinatorial Testing Problem

Computer software is in high demand everywhere in the world. The high dependence on software makes software requirements more complicated. As a result, software testing tasks get costlier and challenging due to a large number of test cases, coupled with the vast number of the system requirements. This challenge presents the need for reduction of the system redundant test cases. A combinatorial testing approach gives an intended result from the optimization of the system test cases. Hence, this study implements a combinatorial testing strategy called Artificial Bee Colony Test Generation (ABC-TG) that helps to get rid of some of the current combinatorial testing strategies. Results obtained from the ABC-TG were benchmarked with the results obtained from existing strategies in order to determine the efficiency of the ABC-TG. Finally, ABC-TG shows the efficiency and effectiveness in terms of generating optimum test cases size of some of the case studies and a comparable result with the existing combinatorial testing strategies