Boosting Spectrum-Based Fault Localization for Spreadsheets with Product Metrics in a Learning Approach

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.