Enhanced neighborhood metric for spreadsheet fault prediction

Abstract

Spreadsheets are widely used in business and scientific domains, yet they are prone to input errors that can lead to significant risks. Faults often occur due to the use of formulas that are syntactically correct but semantically incorrect. This issue is particularly challenging for formula cells that are physically close and exhibit minor logical differences, which traditional fault prediction methods struggle to detect. To address these challenges, this paper introduces an enhanced neighborhood metric approach, which extends traditional formula-based metrics by incorporating neighborhood-based metrics. This approach analyzes the dependencies between adjacent formula cells, considering factors such as formula diversity, content dissimilarity, and structural consistency. This study introduces eight new neighborhood-based spreadsheet indicators to improve fault prediction, building on previous metric-based methods. Extensive experiments conducted on three widely used datasets–Enron, INFO1, and EUSES–demonstrated that integrating the enhanced neighborhood metrics with traditional ones significantly improves fault prediction performance. The approach shows notable improvements in precision, recall, and F1-scores, particularly for medium and large datasets. This study highlights the importance of incorporating neighborhood metrics for spreadsheet fault detection. The enhanced neighborhood metric approach improves fault detection accuracy by capturing subtle logical variations between formula cells that are physically close. This method offers a robust and effective approach for improving the reliability of spreadsheets and can be applied in various real-world data analysis tasks.