Please use this identifier to cite or link to this item: https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4300
Title: Rational Intelligent Project Scheduler for Software Project Management
Authors: Handunge, G.D.P.M.
Issue Date: 29-Jul-2021
Abstract: Software systems have become a vital part in our current society and all of us are getting more dependent on services getting through many different software systems. Nevertheless, developing a good software is always not an easy task and making it a successful business is always a challenge. Among many problems in the software industry, one key problem is how to identify a good software project plan for a given software project? Many software development companies get project from different customers and they have to accurately predict the cost and timeline to make a good bid due to the higher competition in the market. Therefore, making a right software project plan is crucial for the software industry. Software project scheduling is a complex task mainly due to the factors (e.g., time, resources, skills, holidays, etc.) affect on creating a plan. In computer science, this is an optimization problem and it is always a hard problem to find a correct schedule. Nevertheless, Genetic Algorithm (GA) has been identified as a best fit to handle this problem with a higher accuracy. GA being an evolutionary process, it provides an iterative process such that a population will lead to a convergence to achieve the required properties. Therefore, GA based Software Project Scheduling (SPS) is a key research area. This research provides an approach for the research question: how to generate a project schedule for a complex software project as an automated task? This approach includes a series of steps that leads to solve this problem. It was identified the key factors that affect on SPS. First a software project schedule solution is encoded into a binary form. Having binary representation for the real problem a GA process is followed with rank section and elicit selection on a population. This process provides new individuals and each individual is quantitatively measured how quality it is. Having calculated the fitness values of each individual, higher valued candidates were selected for the new population and applied the GA operators (crossover and mutation) to derive new population. Those candidates’ fitness also evaluated and this steps executed as an iterative process until a high quality solution is derived. This approach was evaluated with Turing Test mode and identified the quality of the proposed methodology with GA, It is found that schedules derived through the developed prototype is comparable with what human experts created. Therefore, it is observed that the proposed approach is a good solution for software project scheduling, while some problems identified which needs to address in future work.
URI: http://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4300
Appears in Collections:2018

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