Optimal project feature weights in analogy-based cost estimation: improvement and limitations

Auer, Martin T.; Trendowicz, Adam; Graser, B.; Haunschmid, Ernst; Biffl, Stefan

doi:10.1109/tse.2006.1599418

Cited by 100 publications

(92 citation statements)

References 19 publications

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“…For example, in 2006, Auer et al [9] propose an extensive search to learn the best weights to assign different project features. Also in that year, Menzies et al [10]'s COSEEKMO tool explored thousands of combinations of discretizers, data pre-processors, feature subset selectors, and inductive learners.…”

Section: Introductionmentioning

confidence: 99%

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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: 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%

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

“…Sentas et al (2005) also adopted date-based splitting in two of the three datasets used to compare ordinal regression and stepwise linear regression. Auer et al (2006) and Auer and Biffl (2004) performed an analysis of input attribute weighting methods for analogy-based SEE. They explained that SEE datasets typically grow over time as companies take on new projects.…”

Section: Chronological Splitting Approachesmentioning

confidence: 99%

Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

Minku

Yao

2016

Autom Softw Eng

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Software Effort Estimation (SEE) models can be used for decision-support by software managers to determine the effort required to develop a software project. They are created based on data describing projects completed in the past. Such data could include past projects from within the company that we are interested in (WC projects) and/or from other companies (cross-company, i.e., CC projects). In particular, the use of CC data has been investigated in an attempt to overcome limitations caused by the typically small size of WC datasets. However, software companies operate in non-stationary environments, where changes may affect the typical effort required to develop software projects. Our previous work showed that both WC and CC models of the past can become more or less useful over time, i.e., they can sometimes be helpful and sometimes misleading. So, how can we know if and when a model created based on past data represents well the current projects being estimated? We propose an approach called Dynamic Cross-company Learning (DCL) to dynamically identify which WC or CC past models are most useful for making predictions to a given company at the present. DCL automatically emphasizes the predictions given by these models in order to improve predictive performance. Our experiments comparing DCL against existing WC and CC approaches show that DCL is successful in improving SEE by A preliminary version of this work appeared as Minku and Yao (2012a

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“…In [30], optimum accuracy has been achieved in this area using the feature weightings and comparative methods based on euclidean distance by using filter FS. In addition, some researchers have developed genetic algorithms to achieve a suitable weight for features [31,32].…”

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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Optimal project feature weights in analogy-based cost estimation: improvement and limitations

Cited by 100 publications

References 19 publications

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

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

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