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Data Clone Detection and Visualization in Spreadsheets icse 13

Data Clone Detection and Visualization in Spreadsheets icse 13. Felienne Hermans , Ben Sedee , Martin Pinzger and Arie van Deursen Delft University of Technology. BACKGROUND. Spreadsheets are widely used Copy-paste actions are widely used

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Data Clone Detection and Visualization in Spreadsheets icse 13

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  1. Data Clone Detection and Visualization inSpreadsheetsicse 13 FelienneHermans, Ben Sedee, Martin Pinzger and Arie van Deursen Delft University of Technology

  2. 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.

  3. GOAL • Data clone detection • Data clone visualization

  4. DATA CLONE DETECTION • Algorithm • Cell classification • Lookup creation • Pruning • Cluster finding • Cluster matching

  5. CLONE VISUALIZATION • Dataflow diagrams • Pop-ups

  6. EVALUATION

  7. Comparative Causality: Explaining the DifferencesBetween Executionsicse 13 William N. Sumner XiangyuZhang Purdue University

  8. BACKGROUND • A fine-grained causal inference technique. • Causal State Minimization in Delta Debugging • CSM has its limitations.

  9. LIMITATIONS of CSM • 1. Confounding caused by Partial State Replacement

  10. LIMITATIONS of CSM • 2. Execution Omission • 3. Efficiency

  11. SOLUTION • Confounding & Efficiency • They build a new model without confounding • The model is to simplify the original code and reexecute with this new code

  12. SOLUTION • Execution Omission • Do state replacement both in the correct execution and in the buggy execution.

  13. EVALUATION

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