Zhonghao Guo^*, Xinyue Xu, Xiangxian Chen

Computer Systems Science and Engineering, Vol.48, No.2, pp. 479-509, 2024, DOI:10.32604/csse.2023.043932

Abstract In software testing, the quality of test cases is crucial, but manual generation is time-consuming. Various automatic test case generation methods exist, requiring careful selection based on program features. Current evaluation methods compare a limited set of metrics, which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment. To address this, we propose an evaluation tool, the Test Case Generation Evaluator (TCGE), based on the learning to rank (L2R) algorithm. Unlike previous approaches, our method comprehensively evaluates algorithms by considering multiple metrics, resulting in a more reasoned assessment. The… More >

Mofreh A. Hogo^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2947-2966, 2022, DOI:10.32604/cmc.2022.027300

Abstract Data size plays a significant role in the design and the performance of data mining models. A good feature selection algorithm reduces the problems of big data size and noise due to data redundancy. Features selection algorithms aim at selecting the best features and eliminating unnecessary ones, which in turn simplifies the structure of the data mining model as well as increases its performance. This paper introduces a robust features selection algorithm, named Features Ranking Voting Algorithm FRV. It merges the benefits of the different features selection algorithms to specify the features ranks in the dataset correctly and robustly; based… More >

Rajendra Prasad Nayak¹, Srinivas Sethi², Sourav Kumar Bhoi³, Kshira Sagar Sahoo⁴, Nz Jhanjhi⁵, Thamer A. Tabbakh⁶, Zahrah A. Almusaylim^7,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3513-3529, 2021, DOI:10.32604/cmc.2021.018930

Abstract Reliable vehicles are essential in vehicular networks for effective communication. Since vehicles in the network are dynamic, even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences. In this paper, a Trust-Based Distributed DoS Misbehave Detection Approach (TBDDoSA-MD) is proposed to secure the Software-Defined Vehicular Network (SDVN). A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood. It uses the jamming technique by sending unnecessary signals in the network, as a result, the network performance degrades. Attacked vehicles in that network will no longer… More >