Shweta Singhal¹, Nishtha Jatana², Ahmad F Subahi³, Charu Gupta^4,*, Osamah Ibrahim Khalaf⁵, Youseef Alotaibi⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6755-6774, 2023, DOI:10.32604/cmc.2023.032308

Abstract Software needs modifications and requires revisions regularly. Owing to these revisions, retesting software becomes essential to ensure that the enhancements made, have not affected its bug-free functioning. The time and cost incurred in this process, need to be reduced by the method of test case selection and prioritization. It is observed that many nature-inspired techniques are applied in this area. African Buffalo Optimization is one such approach, applied to regression test selection and prioritization. In this paper, the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization. The proposed algorithm… More >

P. Thilagavathi^*, S. Karthikeyan

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 2035-2050, 2022, DOI:10.32604/iasc.2022.026651

Abstract Test data volume reduction and power consumption during testing time outlines are two main problems for Very Large Scale Integration (VLSI) gadgets. Most the code-based arrangements have been utilized to diminish test data volume, although the most notable way that test data volume is high. The switching action that happens between the test carriers leads would expand power consumption. This work presents a compression/decompression methodology for limiting the amount of test data that should be kept on a tester and conveyed to each center in a System on a Chip (SOC) during a test utilizing the Adaptive Multiscale Transformation Run… More >

Adel Rajab^*

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1743-1761, 2022, DOI:10.32604/iasc.2022.021922

Abstract Wireless sensor networks (WSN) deploy many nodes over an extended area for traffic surveillance, environmental monitoring, healthcare, tracking wildlife, and military sensing. Nodes of the WSN have a limited amount of energy. Each sensor node collects information from the surrounding area and forwards it onto the cluster head, which then sends it on to the base station (BS). WSNs extend the lifetime of the network through clustering techniques. Choosing nodes with the greatest residual energy as cluster heads is based on the idea that energy consumption is periodically distributed between nodes. The sink node gathers information from its environment that… More >