Testing the stationarity assumption in software effort estimation datasets

Bosu, Michael Franklin and MacDonell, Stephen G. and Whigham, Peter (2020) Testing the stationarity assumption in software effort estimation datasets. The 32nd International Conference on Software Engineering & Knowledge Engineering, Virtual, 9 - 19 July, 2020.

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Abstract or Summary

Software effort estimation (SEE) models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software engineering process could mean that this assumption does not hold in at least some cases. This study employs three kernel estimator functions to test the stationarity assumption in three software engineering datasets that have been used in the construction of software effort estimation models. The kernel estimators are used in the generation of non-uniform weights which are subsequently employed in weighted linear regression modeling. Prediction errors are compared to those obtained from uniform models. Our results indicate that, for datasets that exhibit underlying non-stationary processes, uniform models are more accurate than non-uniform models. In contrast, the accuracy of uniform and non-uniform models for datasets that exhibited stationary processes was essentially equivalent. The results of our study also confirm prior findings that the accuracy of effort estimation models is independent of the type of kernel estimator function used in model development.

Item Type:Paper presented at a conference, workshop or other event, and published in the proceedings
Keywords that describe the item:Software effort estimation, software processes, stationarity, kernel estimators, weighted linear regression
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
ID Code:7383
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Deposited On:10 Aug 2020 02:24
Last Modified:10 Aug 2020 02:24

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