Key Technologies of Software Engineering Based on T-ACO Algorithm

• Published in: Mobile information systems Vol. 2022; pp. 1 - 14
• Main Author: Lu, Litao
• Format: Journal Article
• Language: English
• Published: Amsterdam Hindawi 12.09.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Ant colony optimization; Automation; Basic converters; Big Data; Bridges; Economic development; Foraging behavior; Normal distribution; Optimization algorithms; Pheromones; Product development; Software development; Software engineering; Technology assessment
• ISSN: 1574-017X; 1875-905X; 1875-905X
• DOI: 10.1155/2022/4429373

The main core of software engineering key technologies is the development of software services, ensuring the scientificity, security, and stability of the application software engineering system. At present, China’s economic development urgently needs the support of software engineering technology. Based on the T-ACO algorithm, the scientificity of software engineering and the accuracy of data have been significantly improved compared with traditional software engineering technology. It plays an important role in promoting the follow-up software engineering technology. In order to effectively analyze the key technology of engineering software, an improved ant colony algorithm based on T distribution is proposed in this paper. Because the basic ant colony algorithm is easy to fall into the local optimum and the optimization accuracy is low, in the optimization process, at the beginning of the pheromone update, the introduction of the T distribution is helpful for the basic ant colony algorithm to make up for its shortcomings. Adding pheromone variables to the basic ant colony algorithm improves the diversity of the ant colony, thereby eliminating the limitations of local optimal solutions. At the same time, the T-ACO algorithm also improves the search accuracy and convergence speed of automatic data generation in software engineering. In this paper, the performance of the T-ACO algorithm is simulated by experiments. Experimental analysis shows that when the population size is small, the T-ACO algorithm may sometimes not converge to the optimal solution, but when the population size is large (≥50), the T-ACO algorithm may converge to the optimal solution. It can realize the coverage of the total path by the output test case set. While the other two algorithms can achieve full path coverage, they are not stable, resulting in an average coverage between 90% and 100%. The T-ACO algorithm not only has good accuracy in creating test case sets, but also has good algorithm performance, and it is suitable as a multipath test case creation algorithm.