Generating and prioritizing optimal paths using ant colony optimization

• Published in: Computational ecology and software Vol. 5; no. 1; pp. 1 - 15
• Main Author: Mann, Mukesh
• Format: Journal Article
• Language: English
• Published: Hong Kong International Academy of Ecology and Environmental Sciences (IAEES) 01.03.2015; International Academy of Ecology and Environmental Sciences
• Subjects: Algorithms; Ant colony optimization; ant colony optimization (ACO); Ant System (AS); Computer programs; Consumption; Control Flow Graph (CFG); Decision to Decision (DD) graph; Ecology; Formicidae; Optimization; Software reliability; Software testing
• ISSN: 2220-721X; 2220-721X
• DOI: 10.0000/issn-2220-721x-compuecol-2015-v5-0001

The assurance of software reliability partially depends on testing. Numbers of approaches for software testing are available with their proclaimed advantages and limitations, but accessibility of any one of them is a subject dependent. Time is a critical factor in deciding cost of any project. A deep insight has shown that executing test cases are time consuming and tedious activity. Thus stress has been given to develop algorithms which can suggest better pathways for testing. One such algorithm called Path Prioritization -Ant Colony Optimization (PP-ACO) has been suggested in this paper which is inspired by real Ant's foraging behavior to generate optimal paths sequence of a decision to decision (DD) path of a graph. The algorithm does full path coverage and suggests the best optimal sequences of path in path testing and prioritizes them according to path strength.