Exact and Near-miss Clone Detection in Spreadsheet Data

Spreadsheets are used extensively in business across many domains, yet data quality issues from copy-pasting are often overlooked. Clone detection, a technique proven useful in source code analysis, is researched for its applicability to spreadsheet data by author Felienne Hermans. The work explores detecting both exact clones and near-miss clones that differ slightly, which can cause maintenance errors.

Use Cases

• Detecting exact data clones in spreadsheets based on the described copy-paste practice.
• Identifying near-miss clones in spreadsheet data based on the described technique adapted from source code analysis.
• Analyzing the impact of data clones on spreadsheet maintenance effort and error rates as described.
• Developing tools for spreadsheet data quality improvement based on clone detection research.

Strengths

• Focuses on a specific, under-researched aspect of spreadsheet quality: data clones from copy-pasting.
• Applies a proven software engineering method (clone detection) to a new domain (spreadsheet data).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Research work by Felienne Hermans; likely contains findings or a corpus related to spreadsheet clone detection.