Data Clone Detection and Visualization in Spreadsheets icse 13
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Transcript of Data Clone Detection and Visualization in Spreadsheets icse 13
Data Clone Detection and Visualization in
Spreadsheetsicse 13
Felienne Hermans, Ben Sedee, Martin Pinzger and Arie van DeursenDelft University of Technology
BACKGROUND
• Spreadsheets are widely used
• Copy-paste actions are widely used
• If formulas’s values are copied as plain text in a different location, data can be easily out of sync.
GOAL
• Data clone detection
• Data clone visualization
DATA CLONE DETECTION
• Algorithm– Cell classification– Lookup creation– Pruning– Cluster finding– Cluster matching
CLONE VISUALIZATION
• Dataflow diagrams
• Pop-ups
EVALUATION
Comparative Causality: Explaining the Differences
Between Executionsicse 13
William N. Sumner Xiangyu ZhangPurdue University
BACKGROUND
• A fine-grained causal inference technique.
• Causal State Minimization in Delta Debugging
• CSM has its limitations.
LIMITATIONS of CSM
• 1. Confounding caused by Partial State Replacement
LIMITATIONS of CSM
• 2. Execution Omission
• 3. Efficiency
SOLUTION
• Confounding & Efficiency– They build a new model without confounding– The model is to simplify the original code and
reexecute with this new code
SOLUTION
• Execution Omission– Do state replacement both in the correct
execution and in the buggy execution.
EVALUATION