Improved Ant Algorithms for Software Testing Cases Generation
Joint Authors
Yang, Shunkun
Man, Tianlong
Xu, Jiaqi
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-05
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Yang, Shunkun& Man, Tianlong& Xu, Jiaqi. 2014. Improved Ant Algorithms for Software Testing Cases Generation. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1049440
Modern Language Association (MLA)
Yang, Shunkun…[et al.]. Improved Ant Algorithms for Software Testing Cases Generation. The Scientific World Journal No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1049440
American Medical Association (AMA)
Yang, Shunkun& Man, Tianlong& Xu, Jiaqi. Improved Ant Algorithms for Software Testing Cases Generation. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1049440
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049440