Sampling as a Baseline Optimizer for Search-Based Software Engineering

Jianfeng Chen

doi:10.1109/TSE.2018.2790925

Return

Sampling as a Baseline Optimizer for Search-Based Software Engineering

2019-06-01

DOI：10.1109/TSE.2018.2790925

Original

Save

View PDF

Abstract

En 中文

Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. An alternative approach, proposed in this paper, is to start with a very large population and sample down to just the better solutions. We call this method SWAY, short for the sampling way. This paper compares SWAY versus state-of-the-art search-based SE tools using seven models: five software product line models; and two other software process control models (concerned with project management, effort estimation, and selection of requirements) during incremental agile development. For these models, the experiments of this paper show that SWAY is competitive with corresponding state-of-the-art evolutionary algorithms while requiring orders of magnitude fewer evaluations. Considering the simplicity and effectiveness of SWAY, we, therefore, propose this approach as a baseline method for search-based software engineering models, especially for models that are very slow to execute.

Keywords:

Search-based SE

sampling

evolutionary algorithms

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.