一句话：评估心理学启发的快速节俭树（FFT）能否用于软件分析，生成可理解的启发式规则。
论文关注软件分析中模型的可理解性与可操作性。心理科学认为，人类更易理解与自身内部模型匹配的模型，即简洁的启发式规则列表；快速节俭树（Fast-and-Frugal Trees, FFT）是其中一种受心理学家青睐的规则生成器。尽管 FFT 已在多个应用领域取得成功，但尚未被用于软件分析。因此，本文对 FFT 在软件分析中的应用进行评估，探讨其能否为软件分析提供可行动的模型。摘要未给出具体评估结果、数据集或指标。
According to psychological scientists, humans understand models that most match their own internal models, which they characterize as lists of "heuristic"s (i.e. lists of very succinct rules). One such heuristic rule generator is the Fast-and-Frugal Trees (FFT) preferred by psychological scientists. Despite their successful use in many applied domains, FFTs have not been applied in software analytics. Accordingly, this paper assesses FFTs for software analytics.
DOI 原文 ·
@inproceedings{paperbot4658,
title = {Applications of psychological science for actionable analytics},
author = {Di Chen and Wei Fu and Rahul Krishna and Tim Menzies},
booktitle = {FSE},
year = {2018},
doi = {10.1145/3236024.3236050}
}