Abstract
Analogy-Based Estimation (ABE) is one of the promising estimation models used for predicting the software development effort. Researchers proposed different variants of the ABE model, but still, the most suitable procedure could not be produced for accurate estimation. In this study, an artificial Bee colony guided Analogy-Based Estimation (BABE) model is proposed which ensembles Artificial Bee Colony (ABC) with ABE for accurate estimation. ABC produces different weights, out of which the most appropriate is infused in the similarity function of ABE during the stage of model training, which are later used in the testing stage for evaluation. There are six real datasets utilized for simulating the model procedure. Five of these datasets are taken from the PROMISE repository. The predictive performance is improved for BABE over the existing ones. The most significant of its performance is found on the International Software Benchmarking Standards Group (ISBSG) dataset.
Highlights
Estimating the software development effort is a paramount and chaotic activity in project management
The results revealed that the type and size of the dataset do affect the performance of weight optimization models for Analogy-Based Estimation (ABE)
The Bee colony guided Analogy-Based Estimation (BABE) model was produced in this study which ensembles Artificial Bee Colony (ABC) with ABE for feature weight optimization and accurate effort estimation by comparing the targeted project with the historical projects
Summary
Estimating the software development effort is a paramount and chaotic activity in project management. It becomes burdensome and difficult to estimate the development effort of software due to lack of information in the early stage of a project which led researchers to develop non-algorithmic estimation models. Classification And Regression Tree (CART) is another very prominent non-algorithmic estimation method that is used to construct a regression tree based on the past project information where the amount of effort applied is represented by leaves. Analogy-Based Estimation (ABE) is a commonly used non-algorithmic model that estimates the cost of a targeted project by comparing and finding the most related project from the pool of past projects [10]. ESTIMATION BY ANALOGY (ABE) ABE or EBA was introduced as the non-algorithmic estimation method by Shepperd and Schofield [10] It estimates the effort of a new project by comparing it with the historical projects. Where p and p represent the projects to be compared, wi is the weight allocated to the features which can range between 0 to 1. δ is used to retrieve a non-zero result. fi and f i represent the project features while n determines the number of features
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