Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example
This addresses the usability issue for spreadsheet users by automating rule creation, though it is incremental as it builds on existing inductive program synthesis techniques.
The authors tackled the problem of users needing to manually write conditional formatting rules in spreadsheets by introducing CORNET, a system that automatically learns these rules from examples, achieving accurate rule generation as demonstrated in an Excel add-in.
Data management and analysis tasks are often carried out using spreadsheet software. A popular feature in most spreadsheet platforms is the ability to define data-dependent formatting rules. These rules can express actions such as "color red all entries in a column that are negative" or "bold all rows not containing error or failure." Unfortunately, users who want to exercise this functionality need to manually write these conditional formatting (CF) rules. We introduce CORNET, a system that automatically learns such conditional formatting rules from user examples. CORNET takes inspiration from inductive program synthesis and combines symbolic rule enumeration, based on semi-supervised clustering and iterative decision tree learning, with a neural ranker to produce accurate conditional formatting rules. In this demonstration, we show CORNET in action as a simple add-in to Microsoft Excel. After the user provides one or two formatted cells as examples, CORNET generates formatting rule suggestions for the user to apply to the spreadsheet.