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Sequential Model Optimization for Software Effort Estimation
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DOI：10.1109/TSE.2020.3047072.png)
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Abstract
En 中文
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 one 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.
Keywords:
Estimation
Software
Tools
Optimization
Data models
Task analysis
Mathematical model
Effort estimation
COCOMO
hyperparameter tuning
regression trees
sequential model optimization
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