Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation

Keung, Jacky; Kitchenham, Barbara; Jeffery, D. Ross

doi:10.1109/tse.2008.34

Cited by 112 publications

(92 citation statements)

References 21 publications

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“…In other work Keung [24], Li et al [13], and Hall & Holmes [21] review eight difference feature weighting schemes. Closer inspection reveals many more design variants of feature weighting.…”

Section: Eight Feature Weighting Methods and Five Discretizersmentioning

confidence: 99%

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

Kocagüneli

Menzies

Bener

et al. 2012

IIEEE Trans. Software Eng.

181

161

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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.

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Section: Eight Feature Weighting Methods and Five Discretizersmentioning

confidence: 99%

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

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“…In addition, some researchers have developed genetic algorithms to achieve a suitable weight for features [31,32]. In [19], researchers have examined the balance between the features of the old datasets to reduce cost determination, while maintaining accuracy.…”

Section: Non-algorithmicmentioning

confidence: 99%

Bridging the semantic gap for software effort estimation by hierarchical feature selection techniques

Beiranvand

Chahooki

2016

JAIDM

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Software project management is one of the significant activates in the software development process. Software development effort estimation (SDEE) is a challenging task in the software project management. SDEE has been an old activity in computer industry from 1940s, and thus it has been reviewed for several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before a software project contract. Due to the uncertain nature of development estimates, and in order to increase the accuracy, researchers have recently focused on machine learning techniques. Choosing the most effective features to achieve higher accuracy in machine learning is crucial. In this work, for narrowing the semantic gap in SDEE, a hierarchical filter and wrapper feature selection (FS) techniques and fused measurement criteria are developed in a two-phase approach. In the first phase, the two-stage filter FS methods provide start sets for the wrapper FS techniques. In the second phase, a fused criterion is proposed to evaluate the accuracy in wrapper FS techniques. The experimental results show the validity and efficiency of the proposed approach for SDEE over a variety of standard datasets.

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“…The method utilizes Mantel correlation between distance matrix of attribute values and distance matrix of effort values, following the stepwise procedure. This method has a certain limitation as stated and confirmed by Keung et al [16], in that it cannot directly deal with the distance matrix of nominal data individually. So it cannot assess the appropriateness of nominal attribute solely, but alternatively by dividing the best continuous attributes into subgroups according to the number of categories in that nominal attribute.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Keung et al [16] proposed a method called Analogy-X to statistically test the hypothesis of EBA using Mantel correlation. The method utilizes Mantel correlation between distance matrix of attribute values and distance matrix of effort values, following the stepwise procedure.…”

Section: Introductionmentioning

confidence: 99%