| Title: | Improved ant algorithms for software testing cases generation |
| Address: | "School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China" |
| ISSN/ISBN: | 1537-744X (Electronic) 2356-6140 (Print) 1537-744X (Linking) |
| Abstract: | "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 produce 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" |
| Keywords: | "*Algorithms Models, Theoretical *Software/standards;" |
| Notes: | "MedlineYang, Shunkun Man, Tianlong Xu, Jiaqi eng Research Support, Non-U.S. Gov't 2014/06/03 ScientificWorldJournal. 2014; 2014:392309. doi: 10.1155/2014/392309. Epub 2014 May 5" |
