LLM-generated warm starts improve active learning on low- and medium-dimensional software engineering tasks but underperform Gaussian process methods on high-dimensional tasks.
FLASH: A Faster Optimizer for SBSE Tasks
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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).
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Can Large Language Models Improve SE Active Learning via Warm-Starts?
LLM-generated warm starts improve active learning on low- and medium-dimensional software engineering tasks but underperform Gaussian process methods on high-dimensional tasks.