Software process tailoring is the act of customising the existing software process to suit the specific software project. Current practices in software process tailoring consider project characteristics as the sole input to tailor the software process. In addition, it maintains the traditional approach whereby all the project characteristics factors are treated as being equally important. There is a need to shift the traditional software process tailoring approach to a value-centric approach by using a value-based software engineering concept. This study aims to propose a value-based software process tailoring framework to tailor the software process. A review was conducted to analyse the components embedded and input factors in the selected prior studies on software process tailoring. The framework proposed in this study uses value-based factors as input factors to tailor the software process. The framework also considers value prioritisation component, which rank the process elements according to value priority.
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.
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