1 / 14

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

argyle
Download Presentation

Data Clone Detection and Visualization in Spreadsheets icse 13

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related