International Journal of Nonlinear Analysis and Applications

Predicting and speeding up performance testing for application programs with automation

Document Type : Research Paper

Authors

Information Science and Technology Department, UKM, Malaysia

Abstract

Software was created as a result of the importance of measuring programmer performance. The amount of time and storage space required to implement a programmer largely determines its performance. This project used automation entrenched reliable rules to estimate the executive time. Our applied similar sophisticated criteria and software in this study to achieve the same automation results automatically and rapidly. Aside from time and storage space, the prepared software evaluates a program's performance using other criteria such as dependability, documentation, and others. All of these standards help to make sound performance judgments. This study concluded the performance testing of programmer samples written in Python as simple structures that provide a clear and easy starting point for testing programmer performance.

Keywords

[1] A.M. Alghamdi and F.E. Eassa, Software testing techniques for parallel systems, IJCSNS Int. J. Comput. Sci. Network Secur. 19 (2019), no. 4, 176–186.
[2] K. Gao, Simulated software testing process and its optimization considering heterogeneous debuggers and release time, IEEE Access 9 (2021), 38649–38659.
[3] T. Hamilton, Software testing techniques with test case design, https://www.guru99.com/software testingtechniques.html, 2021.
[4] M. Ibrahim and U. Durak, S. G¨otz, L. Linsbauer, I. Schaefer and A. Wortmann, State of the art in software tool qualification with DO-330: A survey, Software Engineering (Satellite Events), Lecture Notes in Informatics (LNI), Gesellschaft f¨ur Informatik, Bonn, 2021.
[5] R.A. Khurma, H. Alsawalqah, I. Aljarah, M.A. Elaziz and R. Damaˇseviˇcius, An enhanced evolutionary software defect prediction method using island moth flame optimization, Mathematics 9 (2021), no. 15, 1–20.
[6] F. Meng, W. Cheng and J. Wang, Semi-supervised software defect prediction model based on tri-training, KSII Trans. Internet Inf. Syst. 15 (2021), no. 11, 4028–4042.
[7] S. Natarajan and M. Chellam, Study on different types of software in library and their evaluation, Indian Highways 11 (2019), no. 4, 26–37.
[8] L. Raamesh, S. Jothi and S. Radhika, Enhancing software reliability and fault detection using hybrid brainstorm optimization-based LSTM model, IETE J. Res. (2022), 1–15. https://doi.org/10.1080/03772063.2022.2069603
[9] S. Shafiee, Y. Wautelet, S.C. Friis, L. Lis, U. Harlou and L. Hvam, Evaluating the benefits of a computer-aided software engineering tool to develop and document product configuration systems, Comput. Ind. 128 (2021), p. 103432.
[10] S. Singh Ghuman, Software testing techniques, IJCSMC 3 (2014), no. 10, 988–993.
[11] D.S. Taley and B. Pathak, Comprehensive study of software testing techniques and strategies, Int. J. Eng. Tech. Res. 9 (2020), no. 8, 817–822.
International Journal of Nonlinear Analysis and Applications
Volume 14, Issue 1
January 2023
Pages 2647-2654
  • Receive Date: 22 December 2022
  • Accept Date: 02 January 2023