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Size doesn't matter? On the value of software size features for effort estimation
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.
Publication titleProceedings of the 8th International Conference on Predictive Models in Software
Department/SchoolSchool of Information and Communication Technology
PublisherACM Digital Library
Place of publicationNew York, USA
Event title8th International Conference on Predictive Models in Software (PROMISE '12)
Event VenueLund, Sweden
Date of Event (Start Date)2012-09-21
Date of Event (End Date)2012-09-22
Rights statementCopyright 2012 ACM