一句话：提出软件故障预测度量的分类体系，统一术语并系统归类各类度量及其属性。
软件故障预测（SFP）领域利用软件度量构建机器学习或统计预测模型，已发展数十年，所用度量数量急剧增加，导致术语不统一、度量难以比较和系统归类。本文为博士研讨会论文，报告作者正在进行的分类体系构建工作。该工作旨在尽可能全面地建立 SFP 度量的分类体系，为每项度量提供缩写、全称、无歧义描述、故障预测粒度（如方法、类）、类别以及使用该度量的研究论文等详细信息。其目标包括标准化领域术语、简化研究者交流，并帮助形成对 SFP 度量的全局理解。目前工作仍在进行中，尚未给出最终分类体系或实验评估。
In the field of Software Fault Prediction (SFP), researchers exploit software metrics to build predictive models using machine learning and/or statistical techniques. SFP has existed for several decades and the number of metrics used has increased dramatically. Thus, the need for a taxonomy of metrics for SFP arises firstly to standardize the lexicon used in this field so that the communication among researchers is simplified and then to organize and systematically classify the used metrics. In this doctoral symposium paper, I present my ongoing work which aims not only to build such a taxonomy as comprehensive as possible, but also to provide a global understanding of the metrics for SFP in terms of detailed information: acronym(s), extended name, univocal description, granularity of the fault prediction (e.g., method and class), category, and research papers in which they were used.
DOI 原文 ·
@inproceedings{paperbot5151,
title = {A taxonomy of metrics for software fault prediction},
author = {Maria Caulo},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3341462}
}