Sequential Model Optimization for Software Process Control
This addresses the challenge of accurate effort estimation in software engineering, particularly for diverse project types, though it is incremental as it builds on existing optimization techniques.
The paper tackles the problem of software effort estimation by proposing ROME, a configuration technique that uses sequential model-based optimization to find the best combination of estimation methods for specific datasets, achieving better performance than state-of-the-art methods on both classic and contemporary projects.
Many methods have been proposed to estimate how much effort is required to build and maintain software. Much of that research assumes a ``classic'' waterfall-based approach rather than contemporary projects (where the developing process may be more iterative than linear in nature). Also, much of that work 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 these drawbacks, we apply a configuration technique called ``ROME'' (Rapid Optimizing Methods for Estimation), which uses sequential model-based optimization (SMO) to find what combination of effort estimation techniques works best for a particular data set. We test this method using data from 1161 classic 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 existing state-of-the-art methods for both classic and contemporary problems. 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 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 classic and contemporary projects.