An Intelligent Recommender and Decision Support System (IRDSS) for Effective Management of Software Projects

Muhammad Hamid, Furkh Zeshan, Adnan Ahmad, Farooq Ahmad, Muhammad Ali Hamza, Zuhaib Ashfaq Khan, Saima Munawar, Hanan Aljuaid

Research output: Contribution to journalArticlepeer-review


The management and estimation of agile projects are challenging tasks for software companies due to their high failure rates. This paper emphasizes how to improve management and estimation challenges in the context of scrum, which is an agile process widely used for the development of small to medium size software projects. The scrum emphasis on code results in spending inadequate time on the estimation process. Mostly, the scrum master, along with the scrum team, estimates the upcoming software projects based on experience or historical data. Many issues can arise in a case where expert judgment is not available or historical data are not properly organized. In this paper, an Intelligent Recommender and Decision Support System (IRDSS) is proposed that can help the scrum master to better estimate an upcoming software project in terms of cost, time, and recommendations of human resources. Formal specification of IRDSS is also performed using the formalism known as Z language. Furthermore, an experiment on fifteen web projects was performed to validate the proposed approach and compared it with Delphi and Planning Poker estimation methods. The overall results indicate that the proposed system can produce better estimation than Planning Poker and Delphi methods by applying MMRE and PRED evaluation. This research opens new directions for the scrum community for the development of software projects within the allocated time and cost.
Original languageEnglish
Pages (from-to)140752 - 140766
Number of pages14
JournalIEEE Access
Publication statusPublished - 21 Jul 2020


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