Software Effort Estimation for Successful Software Application Development

The recent advancements in information and communication technology (ICT) have inspired all the operational domains of both public and private sector enterprise to endorse this technology. Software development plays a crucial role in supporting ICT. Software effort estimation serves as a critical factor in software application development, and it helps application development teams to complete the development process on time and within budget. Many developmental approaches have been used for software effort estimation, but most of them were conventional software methods and therefore failed to produce accurate results when it came to web or mobile effort estimation. This chapter explains different types of software applications, software estimation models, the importance of software effort estimation, and challenges faced in software effort estimation.

Parameter Design for Group Method Data Handling (GMDH) using Taguchi in Software Effort Estimation

Recently, the use of data-driven models is becoming increasingly impactful but has proven to offer best prediction with less knowledge of the geological, hydrological, and physical process behaviour and criteria. A Group Data Handling Model (GMDH) is one of the sub-model common neural network data driven. It was first developed for complex systems with a modelling and recognition algorithm. GMDH is known as a self-organizing heuristic modelling approach. For solving modelling issues involving multiple inputs to single output data, it is very successful. While the GMDH model has been implemented in many modelling fields, some modifications in terms of parameter design have been given little attention. In other respects, Dr. Genichi Taguchi suggested that the Taguchi method for improving the process or product design with the help of significant parameter levels that influence the delivery of the product. In this paper, we evaluated the behaviour of GMDH model based on numbers of neuron per layer, hidden layer, alpha, and train ratio parameters using Taguchi method. Cocomo and Kemerer datasets are used to test our hypothesized scenarios. The result shows that number of neurons, layer and train ratio are the important parameters that affects the performance of the GMDH model.

Flower Pollination Algorithm for Software Effort Coefficients Optimization to Improve Effort Estimation Accuracy

Software effort estimation is one of important area in project management which used to predict effort for each person to develop an application. Besides, Constructive Cost Model (COCOMO) II is a common model used to estimate effort estimation. There are two coefficients in estimating effort of COCOMO II which highly affect the estimation accuracy. Several methods have been conducted to estimate those coefficients which can predict a closer value between actual effort and predicted value. In this paper, a new metaheuristic algorithm which is known as Flower Pollination Algorithm (FPA) is proposed in several scenario of iteration. Besides, FPA is also compared to several metaheuristic algorithm, namely Cuckoo Search Algorithm and Particle Swarm Optimization. After evaluated by using Mean Magnitude of Relative Error (MMRE), experimental results show that FPA obtains the best result in estimating effort compared to other algorithms by reached 52.48% of MMRE in 500 iterations.