Analogy-based Estimation Methods Research Articles

An Intelligent Framework for Estimating Software Development Projects using Machine Learning

The IT industry has faced many challenges related to software effort and cost estimation. A cost assessment is conducted after software effort estimation, which benefits customers as well as developers. The purpose of this paper is to discuss various methods for the estimation of software effort and cost in the context of software engineering, such as algorithmic methods, expert judgment methods, analogy-based estimation methods, and machine learning methods, as well as their different aspects. In spite of this, estimation of the effort involved in software development are subject to uncertainty. Several methods have been developed in the literature for improving estimation accuracy, many of which involve the use of machine learning techniques. A machine learning framework is proposed in this paper to address this challenging problem. In addition to being completely independent of algorithmic models and estimation problems, this framework also features a modular architecture. It has high interpretability, learning capability, and robustness to imprecise and uncertain inputs.

Insightful analogy-based software development effort estimation through selective classification and localization

Accurate development effort estimation is a challenging issue in the management of software projects because it can considerably affect the planning and scheduling of a software project. Over the past few years, many algorithmic and non-algorithmic methods have been proposed to estimate the development effort in the early stages of project. Due to simplicity and estimation capability, analogy-based estimation (ABE) method has been widely accepted by researchers in this area. In spite of the fact that ABE is an efficient estimation method, it suffers from the non-normality and heterogeneous nature of software project datasets. Although prior studies have strived to remedy this issue by weighting, soft computing, and clustering techniques, the estimate accuracy is still not convincing and attempts are ongoing to reach more reliable estimates. The problem is that prior ABE-based studies have not considered the nature of software projects in the estimation process. This paper aims to show the effect of selective project classification and estimation process localization on the performance of ABE. An exhaustive investigation is conducted based on different development types, organization types, and development platforms as three underlying attributes in software projects. An evaluation framework is designed to reveal the ABE performance when it is combined with the proposed classification. A real dataset that includes 448 software projects is utilized for the evaluation purposes. The promising results showed that the estimate accuracy is significantly improved and the estimation process is considerably expedited if the nature of software projects is considered in the ABE method.

Towards Improvement of Analogy-Based Software Development Effort Estimation: A Review

In this paper a systematic review is conducted to investigate the structure, components, techniques, evaluation procedure, and comparison scope related to prior ABE-based studies. The undeniable role of accurate development effort estimation in the success of software project management has attracted the attention of researchers over the past few years. Among various algorithmic and non-algorithmic estimation methods, analogy based estimation (ABE) is a widely accepted method due to its simplicity and estimation capability. This paper investigates the improvement process of ABE method during 2000 to 2012. Six research questions are defined to be addressed through evaluation of prior ABE-based studies. The review domain includes 24 papers selected through a tough filtration process. The results show that improvement of ABE can be performed through adjustment, grey theory, attribute weighting and attribute selection techniques. Moreover, ABE configurations can significantly affect the results.

Increasing the accuracy of software development effort estimation using projects clustering

Software development effort is one of the most important metrics that must be correctly estimated in software projects. Analogy-based estimation (ABE) and artificial neural networks (ANN) are the most popular methods used widely in this field. These methods suffer from inconsistent and irrelevant projects that exist in the software project datasets. In this paper, a new hybrid method is proposed to increase the accuracy of development effort estimation based on the combination of fuzzy clustering, ABE and ANN methods. In the proposed method, the effect of irrelevant and inconsistent projects on estimates is decreased by designing a new framework, in which all the projects are clustered. The quality of training in ANN and the consistency of historical data in ABE are improved using the proposed framework. Two large and real datasets are utilised in order to evaluate the performance of the proposed method and the obtained results are compared to eight other estimation methods. The promising results showed that the proposed method outperformed the other methods on both datasets. The performance metrics of mean magnitude of relative error (MMRE) and the percentage of the prediction (PRED) (0.25) have been improved by average of 51 and 127% in the first dataset, as well as 52 and 94% in the second dataset.

Kernel methods for software effort estimation

Analogy based estimation (ABE) generates an effort estimate for a new software project through adaptation of similar past projects (a.k.a. analogies). Majority of ABE methods follow uniform weighting in adaptation procedure. In this research we investigated non-uniform weighting through kernel density estimation. After an extensive experimentation of 19 datasets, 3 evaluation criteria, 5 kernels, 5 bandwidth values and a total of 2090 ABE variants, we found that: (1) non-uniform weighting through kernel methods cannot outperform uniform weighting ABE and (2) kernel type and bandwidth parameters do not produce a definite effect on estimation performance. In summary simple ABE approaches are able to perform better than much more complex approaches. Hence,—provided that similar experimental settings are adopted—we discourage the use of kernel methods as a weighting strategy in ABE.

A study of the non-linear adjustment for analogy based software cost estimation

Cost estimation is one of the most important but most difficult tasks in software project management. Many methods have been proposed for software cost estimation. Analogy Based Estimation (ABE), which is essentially a case-based reasoning (CBR) approach, is one popular technique. To improve the accuracy of ABE method, several studies have been focusing on the adjustments to the original solutions. However, most published adjustment mechanisms are based on linear forms and are restricted to numerical type of project features. On the other hand, software project datasets often exhibit non-normal characteristics with large proportions of categorical features. To explore the possibilities for a better adjustment mechanism, this paper proposes Artificial Neural Network (ANN) for Non-linear adjustment to ABE (NABE) with the learning ability to approximate complex relationships and incorporating the categorical features. The proposed NABE is validated on four real world datasets and compared against the linear adjusted ABEs, CART, ANN and SWR. Subsequently, eight artificial datasets are generated for a systematic investigation on the relationship between model accuracies and dataset properties. The comparisons and analysis show that non-linear adjustment could generally extend ABE's flexibility on complex datasets with large number of categorical features and improve the accuracies of adjustment techniques.