Software project estimation with machine learning

• Main Authors: Zakaria, Noor Azura, Ismail, Amelia Ritahani, Yakath Ali, Afrujaan, Mohd Khalid, Nur Hidayah, Zainal Abidin, Nadzurah
• Format: Article
• Language: English; English
• Published: Science and Information Organization 2021
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software
• Online Access: http://irep.iium.edu.my/90804/13/90804_Software%20project%20estimation%20with%20machine%20learning_SCOPUS.pdf; http://irep.iium.edu.my/90804/14/90804_Software%20project%20estimation%20with%20machine%20learning.pdf; http://irep.iium.edu.my/90804/; https://thesai.org/Publications/ViewPaper?Volume=12&Issue=6&Code=IJACSA&SerialNo=85; https://dx.doi.org/10.14569/IJACSA.2021.0120685
• Institution: Universiti Islam Antarabangsa Malaysia

This project involves research about software effort estimation using machine learning algorithms. Software cost and effort estimation are crucial parts of software project development. It determines the budget, time and resources needed to develop a software project. One of the well-established software project estimation models is Constructive Cost Model (COCOMO) which was developed in the 1980s. Even though such a model is being used, COCOMO has some weaknesses and software developers still facing the problem of lack of accuracy of the effort and cost estimation. Inaccuracy in the estimated effort will affect the schedule and cost of the whole project as well. The objective of this research is to use several algorithms of machine learning to estimate the effort of software project development. The best machine learning model is chosen to compare with the COCOMO.