Is This Cost Estimate Reliable? -- The Relationship between Homogeneity of Analogues and Estimation Reliability

MacDonell, Stephen G.; Shepperd, Martin

doi:10.1109/esem.2007.31

Cited by 16 publications

(6 citation statements)

References 20 publications

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“…As stated in section 2, the procedure of analogy-based estimation consists of range normalization (step 1), neighborhood projects selection (step 2), and estimated effort computation (step 3). When the effort of neighborhood projects is not homogeneous in step 2, estimation accuracy becomes low [23]. Focusing on this issue, our method identifies an outlier in step 2.…”

Section: Neighborhood's Effort Based Deletionmentioning

confidence: 99%

“…In analogy-based estimation, when the variance of the actual effort of neighborhood projects is large, the estimation accuracy becomes low [23]. Our method identifies a project as an outlier when the effort of the project is significantly higher or lower than other neighborhood projects, and excludes it from the computation of estimated effort.…”

Section: Introductionmentioning

confidence: 99%

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An empirical evaluation of outlier deletion methods for analogy-based cost estimation

Tsunoda

Kakimoto

Monden

et al. 2011

Proceedings of the 7th International Conference on Predictive Models in Software Engineering

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Background: Any software project dataset sometimes includes outliers which affect the accuracy of effort estimation. Outlier deletion methods are often used to eliminate them. However, there are few case studies which apply outlier deletion methods to analogy-based estimation, so it is not clear which method is more suitable for analogy-based estimation. Aim: Clarifying the effects of existing outlier deletion methods (Cook's distance based deletion, LTS based deletion, k-means based deletion, Mantel's correlation based deletion, and EID based deletion) and our method for analogy-based estimation. Method: In the experiment, outlier deletion methods were applied to three kinds of datasets (the ISBSG, Kitchenham, and Desharnais datasets), and their estimation accuracy evaluated based on BRE (Balanced Relative Error). Our method eliminates outliers from the neighborhoods of a target project when the effort is extremely different from other neighborhoods. Results: Deletion methods which are designed to apply to analogy-based estimation (i.e. Mantel's correlation based deletion, EID based deletion, and our method) showed stable performance. Especially, only our method showed over 10% improvement of the average BRE on two datasets. Conclusions: It is reasonable to apply deletion methods designed for analogy-based estimation, and more preferable to apply our method to analogybased estimation.

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Section: Neighborhood's Effort Based Deletionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An empirical evaluation of outlier deletion methods for analogy-based cost estimation

Tsunoda

Kakimoto

Monden

et al. 2011

Proceedings of the 7th International Conference on Predictive Models in Software Engineering

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“…Another study that focuses on data homogeneity is Ohsugi et al (2007). In their hypothesis, they test whether more homogenous analogies for a project produce a more reliable cost estimate.…”

Section: Introductionmentioning

confidence: 97%

“…Thus, it preserves its popularity within the research community and many articles continue to be published, each of which introduces a new perspective on cost estimation (Kitchenham et al 2007;Menzies 2007;Ohsugi et al 2007;Premraj and Zimmermann 2007).…”

mentioning

confidence: 97%

“…Recently, the focus of cost estimation studies has shifted from comparing a number of models on a specific dataset to investigating the relationship between the structure of cost data and the prediction accuracy (Kitchenham et al 2007;Ohsugi et al 2007;Premraj and Zimmermann 2007). Up to now, this issue has been considered as a comparison of either cross-versus within-company methods or cross-versus within-business domain methods and many studies reached different conclusions.…”

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confidence: 97%

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A new perspective on data homogeneity in software cost estimation: a study in the embedded systems domain

2009

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Cost estimation and effort allocation are the key challenges for successful project planning and management in software development. Therefore, both industry and the research community have been working on various models and techniques to accurately predict the cost of projects. Recently, researchers have started debating whether the prediction performance depends on the structure of data rather than the models used. In this article, we focus on a new aspect of data homogeneity, ''cross-versus within-application domain'', and investigate what kind of training data should be used for software cost estimation in the embedded systems domain. In addition, we try to find out the effect of training dataset size on the prediction performance. Based on our empirical results, we conclude that it is better to use cross-domain data for embedded software cost estimation and the optimum training data size depends on the method used.

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Case-Based Reasoning

Trendowicz

Jeffery

2013

Software Project Effort Estimation

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Is This Cost Estimate Reliable? -- The Relationship between Homogeneity of Analogues and Estimation Reliability

Abstract: Abstract

Cited by 16 publications

References 20 publications

An empirical evaluation of outlier deletion methods for analogy-based cost estimation

An empirical evaluation of outlier deletion methods for analogy-based cost estimation

A new perspective on data homogeneity in software cost estimation: a study in the embedded systems domain

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