Improved Ant Algorithms for Software Testing Cases Generation

Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mecha...

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Bibliographic Details
Published in:TheScientificWorld Vol. 2014; no. 2014; pp. 1 - 9
Main Authors: Yang, Shunkun, Xu, Jiaqi, Man, Tianlong
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Wiley
Subjects:
Algorithms
Efficiency
Feedback
Heuristic
Mathematical optimization
Models, Theoretical
Optimization algorithms
Pheromones
Software - standards
Software quality
Software upgrading
Traveling salesman problem
ISSN:2356-6140, 1537-744X, 1537-744X
Online Access:Get full text
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Summary:Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations.
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Academic Editor: Guiwu Wei
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/392309