Abstract—Scheduling of software development and implementation projects is one of the most important and challenging issues facing project managers in the highly competitive software industry. In the market, the final output of software is its price. The price of software is the foundation of the quality and application of that software. As a matter of course, innovation in the idea of software and, consequently, the time spent on designing it can determine its value and price. Therefore, proper scheduling, which is ultimately absolutely crucial for the release of a software application, plays a significant role in the success of that software. There are usually software tools for project management that, using algorithms and collecting data from the program and the conditions of its creation, can identify the best time of release for the software release. In this paper, we try to predict the time needed to make software using meta-heuristic algorithms and through rating tasks, schedule them in a way that the software can be operational in the least time. This paper is a graft between software engineering and meta-heuristic algorithms. The results of various researches have confirmed the superiority of the PSO algorithm to solve this problem compared to other algorithms. This algorithm results in answers with 9 repetitions and within a time span of 27.19 seconds.
Index Terms—Software engineering, estimation of production time, meta-heuristic algorithms, scheduling.
Omid Sojoodi Sheijani is with the Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran (e-mail: firstname.lastname@example.org).
Ali Izadi is with the Department of Computer Engineering, Maragheh Branch, Islamic Azad University, Maragheh, Iran (e-mail: email@example.com).
Cite: Omid Sojoodi Sheijani and Ali Izadi, "Time Optimization during Software Implementation for Timely Delivery Using Meta-Heuristic Algorithms," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 581-585, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).