A study of project selection and feature weighting for analogy based software cost estimation

Li, Yan‐Fu; Xie, Min; Goh, T.N.

doi:10.1016/j.jss.2008.06.001

Cited by 164 publications

(129 citation statements)

References 38 publications

Supporting

Mentioning

128

Contrasting

Order By: Relevance

“…the training projects). Following Kadoda & Shepperd [20], Mendes et al [12], and Li et al [13] we define a baseline ABE called ABE0, as follows.…”

Section: Analogy-based Estimation (Abe)mentioning

confidence: 99%

“…That is, prototypes may be as few as N T = (7, 9, 9)% of the original data. For example, our reading of Li et al [13] is that their genetic algorithms are more an outlier technique than a prototype technique.…”

Section: Three Case Subset Selectorsmentioning

confidence: 99%

“…For example Li et al [13] do not explore all options of their estimators. Rather, they use a genetic algorithm to guide the search for the best project features and the best cases to be 1.…”

Section: Introductionmentioning

confidence: 99%

“…Rather, they use a genetic algorithm to guide the search for the best project features and the best cases to be 1. For example, the effort estimation datasets used in Mendes et al [12], Auer et al [9], Baker [11], this study, and Li et al [13] have median size (13,15,31,33,52), respectively. used in the training data.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

Kocagüneli

Menzies

Bener

et al. 2012

IIEEE Trans. Software Eng.

181

161

View full text Add to dashboard Cite

Abstract-Background: There are too many design options for software effort estimators. How can we best explore them all? Aim: We seek aspects on general principles of effort estimation that can guide the design of effort estimators. Method: We identified the essential assumption of analogy-based effort estimation: i.e. the immediate neighbors of a project offer stable conclusions about that project. We test that assumption by generating a binary tree of clusters of effort data and comparing the variance of super-trees vs smaller sub-trees. Results: For ten data sets (from Coc81, Nasa93, Desharnais, Albrecht, ISBSG, and data from Turkish companies), we found: (a) the estimation variance of cluster sub-trees is usually larger than that of cluster super-trees; (b) if analogy is restricted to the cluster trees with lower variance then effort estimates have a significantly lower error (measured using MRE and a Wilcoxon test, 95% confidence, compared to nearest-neighbor methods that use neighborhoods of a fixed size). Conclusion: Estimation by analogy can be significantly improved by a dynamic selection of nearest neighbors, using only the project data from regions with small variance.

show abstract

“…the training projects). Following Kadoda & Shepperd [20], Mendes et al [12], and Li et al [13] we define a baseline ABE called ABE0, as follows.…”

Section: Analogy-based Estimation (Abe)mentioning

confidence: 99%

Section: Three Case Subset Selectorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

Kocagüneli

Menzies

Bener

et al. 2012

IIEEE Trans. Software Eng.

181

161

View full text Add to dashboard Cite

show abstract

“…Estimation of effort can be carried out in an efficient and accurate manner by collecting relavent software data terms.For the collection of such data,agile methodology (Omar et al, 2011) can be employed which is an accurate, incremental and an iterative one. Li et al (2009a) have proved that the use of neural nets for the prediction of software reliability outperform the traditional statistical systems. Azzeh et al, (2011) have improved the performance of analogy at the early stage of identification process by using fuzzy numbers.…”

Section: Introductionmentioning

confidence: 99%

Detection of Aberrant Data Points for an effective Effort Estimation using an Enhanced Algorithm with Adaptive Features

M.¹

2012

Journal of Computer Science

View full text Add to dashboard Cite

Problem statement:The spiraling growth of IT industry has witnessed an unprecedented change in the software development paradigm, from algorithmic models to machine learning techniques. At present, there are no standard methods to predict the accuracy of software cost estimation, which is an important goal of the software community. Approach: This study proposes a simple and systematic algorithmic procedure for analogy based software cost prediction to detect the aberrant data points. The algorithm is analyzed and correlated with the Desharnais and NASA datasets containing all adaptive features with numerical and categorical variables. Results: The interpreted curves using the above datasets depict a discernible anomaly for the dataset having more categorical variables, thereby indicating the erroneous data points. Conclusion: The elimination of aberrant data points using the new algorithmic method improves the accuracy of software cost estimation using historical data sets.

show abstract

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

Shahpar

Bardsiri

2021

Concurrency and Computation

View full text Add to dashboard Cite

Software development effort estimation is an effective factor in the success or failure of software projects. There are several methods to estimate the effort of software projects, the most common of which is analogy-based estimation (ABE). In this article, a polynomial version of ABE (named PABE) is presented, in which, the project effort is calculated based on a polynomial ensemble of different ABE models. To optimize the controllable parameters of the PABE model, a combined global-local search metaheuristic algorithm based on particle swarm optimization and simulated annealing is utilized in two steps. At the first step, for each similarity and adaptation function, the optimized ABE model is determined by exploiting the optimal value of feature weights, the number of similar projects, and other parameters of the ABE model. Then, at the second step, the amount of effort attained by the optimized models is used for estimating the final effort by the proposed polynomial equation. The proposed PABE method has been successfully executed on five well-known software effort estimation datasets: Maxwell, Albrecht, Cocomo81, Desharnais, and Kemerer. Obtained results show the superiority of the proposed PABE model in terms of accuracy and efficiency compared to other techniques.

show abstract

A study of project selection and feature weighting for analogy based software cost estimation

Cited by 164 publications

References 38 publications

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

Detection of Aberrant Data Points for an effective Effort Estimation using an Enhanced Algorithm with Adaptive Features

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

Contact Info

Product

Resources

About