Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization

TL;DR

This paper proposes an ant colony optimization algorithm to generate minimal, optimal test sequences for software behavior, enhancing testing efficiency and coverage, and compares it with genetic algorithms.

Contribution

It introduces a novel ant colony optimization-based algorithm for transition testing and compares its effectiveness with genetic algorithms.

Findings

Ant colony optimization outperforms genetic algorithms in test sequence minimality.

The proposed method achieves complete software coverage efficiently.

Comparison shows ACO provides better optimized test sequences.

Abstract

Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that's why there is a need to automate the software testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web-based type of software systems. Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing