Robust comparison of similarity measures in analogy based software effort estimation

© 2017 IEEE. Analogy-based software effort estimation (ABE) is a widely-adopted method because of the accuracy it offered as well as its intuitiveness. ABE derives an estimated effort value for a new software project by adapting to the effort values of its similar past projects. Accurately measuring...

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Main Author: Passakorn Phannachitta
Format: Conference Proceeding
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/62661
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-626612018-11-29T07:39:59Z Robust comparison of similarity measures in analogy based software effort estimation Passakorn Phannachitta Computer Science Decision Sciences © 2017 IEEE. Analogy-based software effort estimation (ABE) is a widely-adopted method because of the accuracy it offered as well as its intuitiveness. ABE derives an estimated effort value for a new software project by adapting to the effort values of its similar past projects. Accurately measuring the level of similarity between software project cases is an important process of ABE in regards to whether the retrieved past similar projects are analogous to the new project. However, no one to the best of our knowledge has systematically evaluated and compared the similarity measures for the ABE process. In the present study, 6 similarity measures that have been most commonly appeared in the literatures in a 5-year timeframe up to the time of writing are systematically compared. Based on a comprehensive empirical experiment using 12 industrial datasets consisting of 952 project cases, together with 5 robust performance measures, and subject to a robust statistical test method, we found that simple similarity measures such as Euclidean and Manhattan similarity measures generally offer accurate estimation for software effort estimation datasets. Despite studies in other fields frequently discourage the use of these simple similarity measures, the results of the present study are otherwise supporting them as a crucial part of an ABE model. 2018-11-29T07:38:42Z 2018-11-29T07:38:42Z 2018-02-16 Conference Proceeding 25733214 2373082X 2-s2.0-85054237881 10.1109/SKIMA.2017.8294126 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85054237881&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/62661
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Decision Sciences
spellingShingle Computer Science
Decision Sciences
Passakorn Phannachitta
Robust comparison of similarity measures in analogy based software effort estimation
description © 2017 IEEE. Analogy-based software effort estimation (ABE) is a widely-adopted method because of the accuracy it offered as well as its intuitiveness. ABE derives an estimated effort value for a new software project by adapting to the effort values of its similar past projects. Accurately measuring the level of similarity between software project cases is an important process of ABE in regards to whether the retrieved past similar projects are analogous to the new project. However, no one to the best of our knowledge has systematically evaluated and compared the similarity measures for the ABE process. In the present study, 6 similarity measures that have been most commonly appeared in the literatures in a 5-year timeframe up to the time of writing are systematically compared. Based on a comprehensive empirical experiment using 12 industrial datasets consisting of 952 project cases, together with 5 robust performance measures, and subject to a robust statistical test method, we found that simple similarity measures such as Euclidean and Manhattan similarity measures generally offer accurate estimation for software effort estimation datasets. Despite studies in other fields frequently discourage the use of these simple similarity measures, the results of the present study are otherwise supporting them as a crucial part of an ABE model.
format Conference Proceeding
author Passakorn Phannachitta
author_facet Passakorn Phannachitta
author_sort Passakorn Phannachitta
title Robust comparison of similarity measures in analogy based software effort estimation
title_short Robust comparison of similarity measures in analogy based software effort estimation
title_full Robust comparison of similarity measures in analogy based software effort estimation
title_fullStr Robust comparison of similarity measures in analogy based software effort estimation
title_full_unstemmed Robust comparison of similarity measures in analogy based software effort estimation
title_sort robust comparison of similarity measures in analogy based software effort estimation
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85054237881&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/62661
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