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