Method to estimate parameter values in software prediction models

Miyazaki, Y.; Takanou, A.; Nozaki, Hideaki; Nakagawa, Noriko; Okada, Kensuke

doi:10.1016/0950-5849(91)90139-3

Cited by 53 publications

(24 citation statements)

References 3 publications

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“…There has been some criticism of this measure, in particular that it is unbalanced and penalizes overestimates more than underestimates. For this reason Miyazaki et al [19] propose a balanced mean magnitude of relative error measure as follows:…”

Section: Introductionmentioning

confidence: 99%

Estimating software project effort using analogies

Shepperd

Schofield

1997

IIEEE Trans. Software Eng.

785

703

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Abstract-Accurate project effort prediction is an important goal for the software engineering community. To date most work has focused upon building algorithmic models of effort, for example COCOMO. These can be calibrated to local environments. We describe an alternative approach to estimation based upon the use of analogies. The underlying principle is to characterize projects in terms of features (for example, the number of interfaces, the development method or the size of the functional requirements document). Completed projects are stored and then the problem becomes one of finding the most similar projects to the one for which a prediction is required. Similarity is defined as Euclidean distance in n-dimensional space where n is the number of project features. Each dimension is standardized so all dimensions have equal weight. The known effort values of the nearest neighbors to the new project are then used as the basis for the prediction. The process is automated using a PC-based tool known as ANGEL. The method is validated on nine different industrial datasets (a total of 275 projects) and in all cases analogy outperforms algorithmic models based upon stepwise regression. From this work we argue that estimation by analogy is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques.

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Section: Introductionmentioning

confidence: 99%

Estimating software project effort using analogies

Shepperd

Schofield

1997

IIEEE Trans. Software Eng.

785

703

View full text Add to dashboard Cite

show abstract

“…A large number of different prediction accuracy statistics have been used in the literature (see for example, Conte et al, 1986, Jorgensen, 1995, Lo and Gao, 1997, Miyazaki et al, 1991. However, in a given situation the different accuracy statistics often give contradictory results.…”

Section: Introductionmentioning

confidence: 99%

What accuracy statistics really measure

et al. 2001

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“…For this reason, Miyazaki et al (1991) also proposed a balanced mean magnitude of relative error measure as follows:…”

Section: The Balanced Mmre (Bmmre)mentioning

confidence: 99%

Evaluation of preliminary data analysis framework in software cost estimation based on ISBSG R9 Data

et al. 2008

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Previous research has argued that preliminary data analysis is necessary for software cost estimation. In this paper, a framework for such analysis is applied to a substantial corpus of historical project data (ISBSG R9 data), selected without explicit bias. The consequent analysis yields sets of dominant variables, which are then used to construct project effort estimation models. Performance of the predictors on the raw variables and the extracted sets of variables is then measured in terms of Mean Magnitude of Relative Error (MMRE), Median of Magnitude of Relative Error (MdMRE) and prediction at levels 0.05, 0.1, and 0.25. The results from the comparative evaluation suggest that more accurate prediction models can be constructed for the selected prediction techniques. The framework processed predictor variables are statistically significant, at the 95% confidence level for both parametric techniques and one non-parametric technique. The results are also compared with the latest published results obtained by other research based on the same data set. The comparison indicates that, the models constructed using framework processed data are generally more accurate.

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Method to estimate parameter values in software prediction models

Cited by 53 publications

References 3 publications

Estimating software project effort using analogies

Estimating software project effort using analogies

What accuracy statistics really measure

Evaluation of preliminary data analysis framework in software cost estimation based on ISBSG R9 Data

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