Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

Madhumita Panda,Anand Nayyar,Sujata Dash,Muhammad Bilal,Raja Majid Mehmood

doi:10.1109/access.2020.3026911

Abstract

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.

Highlights

The software development organizations spend more than two third of the project development cost on product testing
This work has much similarity with our work in terms of the model-based testing approach using unified modelling language (UML) diagrams as well as hybrid algorithms, but this approach cannot be compared with our work, as our objective is to generate test data for every feasible path targeting transition path coverage, in our case the case study is the benchmark triangle classification problem having four paths and in the above work the case study is for ATM withdrawal operation, and their objective is to select only one path sequence having minimum cost
This paper proposes a novel hybrid Firefly Algorithm (FA)-Differential Evolution (DE) framework to generate optimized test suits targeting path-based coverage criteria of testing

Summary

Introduction

The software development organizations spend more than two third of the project development cost on product testing. The main intention of testing is to define some specific set of test suites that are capable enough to reveal the hidden errors/mistakes associated with the software under test avoiding bugs or system failures in future [37], [53]. The two most universally adapted testing strategies followed by testers are functional testing commonly known as black box testing and structural testing, popularly known as white box testing [67]–[71]. The popularity of the object-oriented programming concept is due to its modular structure and specific features like encapsulation, polymorphism, inheritance, dynamic binding etc. The object-oriented testing paradigm, popularly known as grey box testing was introduced in late 80s, the main challenges and complexities encountered in this approach is the testing of the specific

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Conclusion