Model to estimate the software development effort based on in‐depth analysis of project attributes

Abstract

Over the past years, numerous models have been proposed to estimate the development effort in the early stages of a software project. The existing models have mostly relied on soft computing techniques and weighting methods. Although they have reduced the complexity and vagueness of software project attributes, attempts are ongoing to develop more accurate and reliable estimation models. This paper is concentrated on selective classification of software projects based on underlying attributes to localise the development effort estimation process in a widely used model called analogy-based estimation (ABE). The proposed model is a combination of ABE, selective classification and a weighting system in which the attributes of different software projects are assigned different weights. In fact, the process of attribute weighting is customised based on the nature of project being estimated. A real data set was utilised to evaluate the performance of the proposed model. A comparison between the estimates achieved by the proposed model and those obtained from other well-known effort estimation models showed that the proposed model substantially improves the performance metrics. Along with the improvement of accuracy, the proposed model is able to be used in an extensive domain of software projects.

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

“…There are various similarity functions in the ABE methods, for example, Euclidean (EUC) similarity function, 5,13,14, Manhattan (MHT) similarity function, 7,18,57,58,[60][61][62][63][64][65][66] maximum distance similarity function, 8,67,68 Minkowski (MKS) similarity function, 46,57 fuzzy, 69,70 and optimized induced learning (OIL). 71 The performance of the different similarity functions may vary based on the type of features (numerical, ordinal, or nominal) and the distribution of data samples in N-dimensional feature space.…”

“…7,14,20,37,40,50 In the ABE methods, the effort of a new project can be estimated based on the efforts of K similar projects utilizing an adaptation function. In the majority of the previous ABE methods, mean, 5,14,[36][37][38][39][40][41][42][43][44][45][46][47][49][50][51][52][53][54]56,57,64,68 median, 8,67 and inverse rank weighted mean 7,18,48,51,55,62,63,66 are the mostly used adaptation functions.…”

How software size influence productivity and project duration

“…There are many software sizing methods viz. function points, use case points, story points (for agile projects), object based count [3]- [6] etc. but IFPUG function point is the most widely used and acceptable software sizing method.…”

The state‐of‐the‐art in software development effort estimation

“…Khatibi et al have proposed the similar model as described by Bardsiri and Khatibi but with some modification. Khatibi et al have used ANOVA and ANCOVA to determine the weight of each project attribute.…”

“…They used firefly to optimize the values of the coefficients of basic forms of COCOMO 81 and COCOMO II models. Their study proves that firefly algorithm outperforms the other optimization algorithms' results in much less number of iterations on a NASA data set.Khatibi et al52 have proposed the similar model as described by Bardsiri and Khatibi46 but with some modification. Khatibi et al have used ANOVA and ANCOVA to determine the weight of each project attribute.…”