A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

Bahman Arasteh (), Keyvan Arasteh, Farzad Kiani, Seyed Salar Sefati, Octavian Fratu, Simona Halunga and Erfan Babaee Tirkolaee ()
Additional contact information
Bahman Arasteh: Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul 34460, Turkey
Keyvan Arasteh: Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul 34460, Turkey
Farzad Kiani: Computer Engineering Department, Faculty of Engineering, Fatih Sultan Mehmet Vakif University, Istanbul 34445, Turkey
Seyed Salar Sefati: Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 060042 Bucuresti, Romania
Octavian Fratu: Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 060042 Bucuresti, Romania
Simona Halunga: Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 060042 Bucuresti, Romania
Erfan Babaee Tirkolaee: Department of Industrial Engineering, Istinye University, Istanbul 34396, Turkey

Mathematics, 2024, vol. 12, issue 2, 1-18

Abstract: The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program’s branch coverage. The proposed method was implemented along with the previous one. The experiments’ results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.

Keywords: bioinspired testing method; discretized bat optimization algorithm; branch coverage; stability; success rate (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/2/186/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/2/186/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:2:p:186-:d:1314216

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().