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