FLASH: A Faster Optimizer for SBSE Tasks

TL;DR

FLASH is a decision tree-based optimizer for search-based software engineering that significantly reduces evaluation time, enhances model simplicity, and maintains competitive optimization effectiveness across various case studies.

Contribution

Introduces FLASH, a novel decision tree-based optimizer that is faster, more interpretable, and scalable compared to existing algorithms in SBSE tasks.

Findings

FLASH is the fastest optimizer, often requiring less than 1% evaluations of evolutionary algorithms.

FLASH produces more succinct and comprehensible models.

FLASH scales to more complex models and always terminates, unlike some state-of-the-art algorithms.

Abstract

Most problems in search-based software engineering involve balancing conflicting objectives. Prior approaches to this task have required a large number of evaluations- making them very slow to execute and very hard to comprehend. To solve these problems, this paper introduces FLASH, a decision tree based optimizer that incrementally grows one decision tree per objective. These trees are then used to select the next best sample. This paper compares FLASH to state-of-the-art algorithms from search-based SE and machine learning. This comparison uses multiple SBSE case studies for release planning, configuration control, process modeling, and sprint planning for agile development. FLASH was found to be the fastest optimizer (sometimes requiring less than 1% of the evaluations used by evolutionary algorithms). Also, measured in terms of model size, FLASH's reasoning was far more succinct and comprehensible. Further, measured in terms of finding effective optimization, FLASH's recommendations were highly competitive with other approaches. Finally, FLASH scaled to more complex models since it always terminated (while state-of-the-art algorithm did not).