TY - JOUR

T1 - Test suit generation for object oriented programs

T2 - A hybrid firefly and differential evolution approach

AU - Panda, Madhumita

AU - Dash, Sujata

AU - Nayyar, Anand

AU - Bilal, Muhammad

AU - Mehmood, Raja Majid

PY - 2020

Y1 - 2020

N2 - In model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and optimizing the test suite generation process of software testing. This paper proposes a concrete model-based testing framework; using UML behavioral state chart model along with the hybrid version of the two most popular nature inspired algorithms, Firefly algorithm (FA) and Differential Algorithm (DE). The hybrid algorithm is adopted to generate optimized test suits for the benchmark triangle classification problem. Experimental results evidently show that the hybrid FA-DE search algorithm outperforms the individual model-based Firefly and Differential Evolution algorithm’s performances in terms of time complexity, better exploration and exploitation as well as variations in test case generation process. The framework generates optimized test data for complete transition path coverage of the available feasible paths of the example problem.

AB - In model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and optimizing the test suite generation process of software testing. This paper proposes a concrete model-based testing framework; using UML behavioral state chart model along with the hybrid version of the two most popular nature inspired algorithms, Firefly algorithm (FA) and Differential Algorithm (DE). The hybrid algorithm is adopted to generate optimized test suits for the benchmark triangle classification problem. Experimental results evidently show that the hybrid FA-DE search algorithm outperforms the individual model-based Firefly and Differential Evolution algorithm’s performances in terms of time complexity, better exploration and exploitation as well as variations in test case generation process. The framework generates optimized test data for complete transition path coverage of the available feasible paths of the example problem.

KW - Differential evolution

KW - Firefly algorithm

KW - Hybrid FA-DE algorithm

KW - Model-based testing

KW - Object oriented testing

KW - Path coverage

KW - Search-based testing

UR - http://www.scopus.com/inward/record.url?scp=85102829057&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2020.3026911

DO - 10.1109/ACCESS.2020.3026911

M3 - Journal article

AN - SCOPUS:85102829057

VL - 8

SP - 179167

EP - 179188

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -