# FLASH: A Faster Optimizer for SBSE Tasks

arXiv: https://arxiv.org/abs/1705.05018 · HTML: https://mlsanity.com/paper/1705.05018/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2017.

## Built on by (most cited first, 3 of 3)

- [Online GANs for Automatic Performance Testing](https://mlsanity.com/paper/2104.11069/) (2021)
- [FRUGAL: Unlocking SSL for Software Analytics](https://mlsanity.com/paper/2108.09847/) (2021)
- [Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics](https://mlsanity.com/paper/2302.01997/) (2023)

## Abstract (arXiv)

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).