CACHECA: A Cache Language Model Based Code Suggestion Tool
Christine Franks, Zhaopeng Tu, Premkumar Devanbu, Vincent Hellendoorn. ICSE 2015
Nearly every Integrated Development Environment includes a form of code completion. The suggested completions (“suggestions”) are typically based on information available at compile time, such as type signatures and variables in scope. A statistical approach, based on estimated models of code patterns in large code corpora, has been demonstrated to be effective at predicting tokens given a context. In this demo, we present CACHECA, an Eclipse plugin that combines the native suggestions with a statistical suggestion regime. We demonstrate that a combination of the two approaches more than doubles Eclipse’s suggestion accuracy. A video demonstration is available at https://www.youtube.com/watch?v=3INk0N3JNtc.
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