Applications of different metaheuristic techniques for finding optimal tst order during integration testing of object oriented systems and their comparative study

TL;DR

This paper explores the use of genetic algorithms and other metaheuristic techniques to optimize test order during integration testing of object-oriented systems, focusing on minimizing stub complexity and comparing different approaches.

Contribution

It introduces a novel GA-based method using a class dependency graph to model dependencies and costs, and provides a comparative analysis with existing techniques.

Findings

GA approach effectively reduces stub complexity

Metaheuristic techniques show varied performance

Comparison highlights strengths and weaknesses of each method

Abstract

In recent past, a number of researchers have proposed genetic algorithm (GA) based strategies for finding optimal test order while minimizing the stub complexity during integration testing. Even though, metaheuristic algorithms have a wide variety of use in various medium to large size optimization problems [21], their application to solve the class integration test order (CITO) problem [12] has not been investigated. In this research paper, we propose to find a solution to CITO problem by the use of a GA based approach. We have proposed a class dependency graph (CDG) to model dependencies namely, association, aggregation, composition and inheritance between classes of unified modeling language (UML) class diagram. In our approach, weights are assigned to the edges connecting nodes of CDG and then these weights are used to model the cost of stubbing. Finally, we compare and discuss the empirical results of applying our approach with existing graph based and metaheuristic techniques to the CITO problem and highlight the relative merits and demerits of the various techniques.