With the rapid development of cloud storage, cloud users are willing to store data in the cloud storage system, and at the same time, the requirements for the security, integrity, and availability of data storage are getting higher and higher. Although many cloud audit schemes have been proposed, the data storage overhead is too large and the data cannot be dynamically updated efficiently when most of the schemes are in use. In order to solve these problems, a cloud audit scheme for multi-copy dynamic data integrity based on red-black tree full nodes is proposed. This scheme uses ID-based key authentication, and improves the classic Merkel hash tree MHT to achieve multi-copy storage and dynamic data manipulation, which improves the efficiency of real-time dynamic data update (insertion, deletion, modification). The third-party audit organization replaces users to verify the integrity of data stored on remote cloud servers, which reduces the computing overhead and system communication overhead. The security analysis proves that the security model based on the CDH problem and the DL problem is safe. Judging from the results of the simulation experiment, the scheme is safe and efficient.
Software testing plays an important role in improving the quality of software, but the design of test cases requires a lot of manpower, material resources, and time, and designers tend to be subjective when designing test cases. To solve this problem and make the test cases have objectivity and greater coverage, a branch coverage test case automatic generation method based on genetic algorithm and RBF neural network algorithm (GAR) is proposed. In terms of test case generation, based on the genetic algorithm optimized in this paper, a certain number of test case samples are randomly selected to train the RBF neural network to simulate the fitness function and to calculate the individual fitness value. The experiment uses 7 C language codes to automatically generate test cases and compares the experimental data generated by the branch coverage test case generation method based on adaptive genetic algorithm (PDGA), traditional genetic algorithm (SGA), and random test generation method (random) to evaluate the proposed algorithm. The experimental results show that the method is feasible and effective, the branch coverage is increased in the generation of test cases, and the number of iterations of the population is less.
Mutation testing is an effective defect-based software testing method, but a large number of mutants lead to expensive testing costs, which hinders the application of variation testing in industrial engineering. To solve this problem and enable mutation testing to be applied in industrial engineering, this paper improves the method of identifying redundant mutants based on data flow analysis and proposes the inclusion relationship between redundant mutants, so that the redundancy rate of mutants is reduced. In turn, the cost of mutation testing can be reduced. The redundant mutants identification method based on definition and reference of variables (ImReMuDF) was validated and evaluated using 8 C programs. The minimum improvement in redundant mutant identification rate was 34.0%, and the maximum improvement was 71.3% in the 8 C programs tested, and the verification results showed that the method is feasible and effective and has been improved in reducing redundant mutants and effectively reducing the execution time of mutation testing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.