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Size doesn't matter? On the value of software size features for effort estimation


Kocaguneli, E and Menzies, T and Hihn, J and Kang, BH, Size doesn't matter? On the value of software size features for effort estimation, Proceedings of the 8th International Conference on Predictive Models in Software, 21-22 September 2012, Lund, Sweden, pp. 89-98. ISBN 978-1-4503-1241-7 (2012) [Refereed Conference Paper]

Copyright Statement

Copyright 2012 ACM

DOI: doi:10.1145/2365324.2365336


Background: Size features such as lines of code and function points are deemed essential for effort estimation. No one questions under what conditions size features are actually a "must".

Aim: To question the need for size features and to propose a method that compensates their absence.

Method: A base lineanalogy-based estimation method(1NN)and a state-of-the-art learner (CART) are run on reduced (with no size features) and full (with all features) versions of 13 SEE data sets. 1NN is augmented with a popularity-based pre-processor to create "pop1NN". The performance of pop1NN is compared to 1NN and CART using 10-way cross validation w.r.t. MMRE, MdMRE, MAR, PRED(25), MBRE, MIBRE, and MMER.

Results: Without any pre-processor, removal of size features decreases the performance of 1NN and CART. For 11 out of 13 data sets, pop1NN removes the necessity of size features. pop1NN (using reduced data) has a comparable performance to CART (using full data).

Conclusion: Size features are important and their use is endorsed. However, if there are insufficient means to collect software size metrics, then the use of methods like pop1NN may compensate for size metrics with only a small loss in estimation accuracy.

Item Details

Item Type:Refereed Conference Paper
Keywords:lines of code, function points, instance selection, popularity, analogy- based estimation, k-NN
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Pattern recognition
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Menzies, T (Associate Professor Tim Menzies)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:81912
Year Published:2012
Deposited By:Information and Communication Technology
Deposited On:2013-01-11
Last Modified:2018-02-28

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