No abstract

Software testing is required to detect the faults and to ensure the quality of the software under development. Usually, test suites are used to evaluate the software system during the software development cycle. But often test suites contain more redundant test cases due to overlapped test objectives. So, the test-suite reduction is an important step to reduce the number of test cases so as to satisfy the entire objectives with less computational cost. Literature presents different methods to select the suitable test suites of optimal subsets for regression testing. Accordingly, this research aims to develop an effective test suite reduction approach for regression testing. The proposed algorithm (GTAP) is newly designed using TAP measure and greedy search algorithm. This algorithm uses TAP-measure which is specially developed for measuring the importance of test cases. The performance of the GTAP algorithm is evaluated using four different evaluation metrics with eleven subject programs available in SIR repository. From the experimentation, the average performance of the proposed GTAP algorithm in all the programs is 93.07% which is higher than the DIV-GA which obtained the value of 90.27%.

Researchers have investigated different approaches to maintain the minimum cost and effort in regression testing. Here, test suite reduction is a common technique to decrease the cost of regression testing by removing the redundant test cases from the test suite and then, obtaining a representative set of test cases that still yield a high level of code coverage. Accordingly, here, the authors have developed two various techniques for test suite reduction. In the first technique, ATAP measure is newly developed to find the reduced test suite with the help of greedy search algorithm. In the second technique, DIV-TBAT (DIVersity-based BAT) algorithm is newly devised based on the mechanisms of Boolean logic within BAT algorithm which improve diversity during the search process. The proposed techniques are experimented using eight programs from SIR subject programs and the performance study is conducted using nine different evaluation metrics based on different research questions. The comparative analysis is performed with the existing algorithms like GreedyRatio, GreedyEIrreplaceability, diversity-based genetic algorithm, TBAT, and TAP, to prove the performance improvement over the eight software programs considered.

Routing in the Internet of Things (IoT) renders the protection against various network attacks as any attacker intrudes the routing mechanism for establishing the destructive mechanisms against the network, which insists the essentiality of the security protocols in IoT. Thus, the paper proposes a secure protocol based on an optimization algorithm, Monarch-Earthworm Algorithm (Monarch-EWA), which is the modification of the Monarch Butterfly algorithm using the Earthworm Optimization Algorithm (EWA) in order to render effective security to the network. Initially, the effective nodes are selected using the Deep Convolutional Neural Network (deep CNN) classifier based on the factors, trust and energy of the node, and stochastic gradient descent algorithm trains the deep CNN classifier. The secure nodes are involved in routing for which the secure multipath is chosen optimally using the proposed Monarch-EWA, which chooses the secure multipath based on the factors, energy and trust. The analysis of the proposed method in the presence of attacks, such as black hole, message replicate and distributed denial of service, reveals that the proposed method outperformed the existing methods. The proposed Monarch-EWA protocol acquired the maximal energy, throughput and detection rate of 0.2268 J, 48.2759% and 82.6231%, respectively, with the minimal delay of 0.0959 ms.

Mobile ad hoc networks (MANETs) routing is a very challenging task because of the dynamic nature of the network. The linking of provisional communication assures based on the infrastructure of MANET, however, there is no centralised monitoring process for making the routing in MANETs in terms of trust and security. Therefore, the stability routing is not considered, which may break easily in dynamic MANETs. Thus, this paper introduces a trust-based secure routing protocol using the proposed atom whale optimisation algorithm (AWOA), which is the trust-aware routing protocol. The developed atom whale optimisation is utilised to select the optimal route with respect to the trust factors, like average encounter rate (AER), and successful cooperation frequency (SCF), integrity factor, and the forwarding rate. Moreover, secure routing is performed between the nodes using the proposed AWOA. The AWOA is the integration of atom search optimisation (ASO), and whale optimisation algorithm (WOA) that inherits the faster global convergence. The fitness function is newly modelled considering mobility, and trust factors. The proposed AWOA outperformed other methods with a minimal end-to-end delay of 0.0083 sec, maximal packet delivery ratio (PDR) of 97.73%, and the maximal throughput of 85.05% respectively.