Spreadsheets are one of the most commonly used programming environments, and are widely deployed in domains like finance where errors can have catastrophic consequences. We present a static analysis specifically designed to find spreadsheet formula errors. Our analysis directly leverages the rectangular character of spreadsheets. It uses an information-theoretic approach to identify formulas that are especially surprising disruptions to nearby rectangular regions. We present ExceLint, an implementation of our static analysis for Microsoft Excel. We demonstrate that ExceLint is fast and effective: across a corpus of nearly 70 spreadsheets, ExceLint takes a median of 8 seconds per spreadsheet, and it significantly outperforms the state of the art analysis.
Thu 8 Nov Times are displayed in time zone: Guadalajara, Mexico City, Monterrey change
15:30 - 15:52 Talk | DeepBugs: A Learning Approach to Name-based Bug Detection OOPSLA | ||
15:52 - 16:15 Talk | ExceLint: Automatically Finding Spreadsheet Formula Errors OOPSLA | ||
16:15 - 16:37 Talk | Finding Code That Explodes Under Symbolic Evaluation OOPSLA | ||
16:37 - 17:00 Talk | FlashProfile: A Framework for Synthesizing Data Profiles OOPSLA Saswat PadhiUniversity of California, Los Angeles, Prateek JainMicrosoft Research Lab, India, Daniel PerelmanUniversity of Washington, USA, Alex PolozovMicrosoft Research, Sumit GulwaniMicrosoft Research, Todd MillsteinUniversity of California, Los Angeles |