一句话：将基于频谱的故障定位与基于电子表格度量的机器学习预测模型结合，显著提升电子表格故障定位的准确率并减少无效检查。
电子表格中的故障在实践中并不少见，且可能造成严重后果。近年来已有工作将基于频谱的故障定位（SFL）迁移到电子表格领域，虽然效果不错，但受限于电子表格计算结构，SFL 常会给出大量假定故障概率相同的候选单元格，仍需人工逐一检查。本文提出将 SFL 与基于电子表格度量的故障预测方法结合，采用机器学习（ML）途径，训练监督模型时使用两类正交特征：一是 SFL 中用于计算相似度系数的变量，二是已有工作中被证明对错误公式具有良好预测能力的电子表格度量。作者在一个广泛使用的含故障电子表格语料库上进行了实验，结果表明该组合模型在浪费工作量（wasted effort）与准确率两项指标上均显著优于基线，有效改善了故障定位性能。
Faults in spreadsheets are not uncommon and they can have significant negative consequences in practice. Various approaches for fault localization were proposed in recent years, among them techniques that transferred ideas from spectrum-based fault localization (SFL) to the spreadsheet domain. Applying SFL to spreadsheets proved to be effective, but has certain limitations. Specifically, the constrained computational structures of spreadsheets may lead to large sets of cells that have the same assumed fault probability according to SFL and thus have to be inspected manually. In this work, we propose to combine SFL with a fault prediction method based on spreadsheet metrics in a machine learning (ML) approach. In particular, we train supervised ML models using two orthogonal types of features: (i) variables that are used to compute similarity coefficients in SFL and (ii) spreadsheet metrics that have shown to be good predictors for faulty formulas in previous work. Experiments with a widely-used corpus of faulty spreadsheets indicate that the combined model helps to significantly improve fault localization performance in terms of wasted effort and accuracy.
@inproceedings{paperbot7173,
title = {Boosting Spectrum-Based Fault Localization for Spreadsheets with Product Metrics in a Learning Approach},
author = {Adil Mukhtar and Birgit Hofer and Dietmar Jannach and Franz Wotawa and Konstantin Schekotihin},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3559546}
}