SpreadCluster: Recovering Versioned Spreadsheets through Similarity-Based Clustering

TL;DR

SpreadCluster is an automated clustering method that groups versioned spreadsheets based on learned similarity features, significantly improving accuracy over filename-based methods and enabling the creation of larger, more comprehensive versioned spreadsheet corpora.

Contribution

The paper introduces SpreadCluster, a novel clustering algorithm that automatically identifies versioned spreadsheets using feature similarity, outperforming existing filename-based approaches.

Findings

SpreadCluster achieves higher precision and recall than filename-based clustering.

It successfully clusters spreadsheets across multiple corpora, including VEnron, FUSE, and EUSES.

The resulting VEnron2 corpus is significantly larger and more comprehensive.

Abstract

Version information plays an important role in spreadsheet understanding, maintaining and quality improving. However, end users rarely use version control tools to document spreadsheet version information. Thus, the spreadsheet version information is missing, and different versions of a spreadsheet coexist as individual and similar spreadsheets. Existing approaches try to recover spreadsheet version information through clustering these similar spreadsheets based on spreadsheet filenames or related email conversation. However, the applicability and accuracy of existing clustering approaches are limited due to the necessary information (e.g., filenames and email conversation) is usually missing. We inspected the versioned spreadsheets in VEnron, which is extracted from the Enron Corporation. In VEnron, the different versions of a spreadsheet are clustered into an evolution group. We observed that the versioned spreadsheets in each evolution group exhibit certain common features (e.g., similar table headers and worksheet names). Based on this observation, we proposed an automatic clustering algorithm, SpreadCluster. SpreadCluster learns the criteria of features from the versioned spreadsheets in VEnron, and then automatically clusters spreadsheets with the similar features into the same evolution group. We applied SpreadCluster on all spreadsheets in the Enron corpus. The evaluation result shows that SpreadCluster could cluster spreadsheets with higher precision and recall rate than the filename-based approach used by VEnron. Based on the clustering result by SpreadCluster, we further created a new versioned spreadsheet corpus VEnron2, which is much bigger than VEnron. We also applied SpreadCluster on the other two spreadsheet corpora FUSE and EUSES. The results show that SpreadCluster can cluster the versioned spreadsheets in these two corpora with high precision.