AN IMPLEMENTATION OF SOFTWARE EFFORT DURATION AND COST ESTIMATION WITH STATISTICAL AND MACHINE LEARNING APPROACHES

In software industry estimation of effort, cost (EDC) and duration is a troublesome procedure. The exertion itself is in charge of experiencing trouble in evaluating EDC. In any software estimation process, the preeminent advance is to characterize and comprehends the framework to be assessed. Software cost estimation algorithmic methods are estimating by analogy, expert judgment method, price to win method, topdown method, bottom-up method are developed by researchers in the field of software engineering. Any methods are not superior to the other methods. In fact, the qualities and shortcomings of those strategies are differentiating to one another. Presently there are two types of software EDC models to estimate the software those are statistical approaches and machine learning approaches. Many of the software cost estimation methods follows statistical approaches, which are not having the capability to present causes and strong or accurate results. Machine learning techniques are suitable in software engineering because it produces accurate results, which estimates by training rules and iterations. Machine learning techniques resolve the challenges like developing programs in a computer and to give the effective outputs by using the experience