Paper Info
Title
Sequential Model Optimization for Software Effort Estimation
Abstract
Many methods have been proposed to estimate how much effort is required to build and maintain software. Much of that research tries to recommend a single method – an approach that makes the dubious assumption that one method can handle the diversity of software project data. To address this drawback, we apply a configuration technique called “ROME” (Rapid Optimizing Methods for Estimation), which uses sequential model-based optimization (SMO) to find what configuration settings of effort estimation techniques work best for a particular data set. We test this method using data from 1161 traditional waterfall projects and 120 contemporary projects (from GitHub). In terms of magnitude of relative error and standardized accuracy, we find that ROME achieves better performance than the state-of-the-art methods for both traditional waterfall and contemporary projects. In addition, we conclude that we should not recommend <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one</i> method for estimation. Rather, it is better to search through a wide range of different methods to find what works best for the local data. To the best of our knowledge, this is the largest effort estimation experiment yet attempted and the only one to test its methods on traditional waterfall and contemporary projects.
Year
DOI
Venue
2022
10.1109/TSE.2020.3047072
IEEE Transactions on Software Engineering
Keywords
DocType
Volume
Effort estimation,COCOMO,hyperparameter tuning,regression trees,sequential model optimization
Journal
48
Issue
ISSN
Citations 
6
0098-5589
0
PageRank 
References 
Authors
0.34
46
4
Name
Order
Citations
PageRank
Tianpei Xia100.34
Rui Shu200.34
Xipeng Shen32025118.55
Tim Menzies42886151.44