DeepDelta: Learning to Repair Compilation Errors
Ali Mesbah, Andrew Rice, Emily Johnston, Nick Glorioso, Edward Aftandilian.. 2019
Programmers spend a substantial amount of time manually repairing code that does not compile. We observe that the repairs for any particular error class typically follow a pattern and are highly mechanical. We propose a novel approach that automatically learns these patterns with a deep neural network and suggests program repairs for the most costly classes of build-time compilation failures. We describe how we collect all build errors and the human-authored, in-progress code changes that cause those failing builds to transition to successful builds at Google. We generate an AST diff from the textual code changes and transform it into a domain-specific language called Delta that encodes the change that must be made to make the code compile. We then feed the compiler diagnostic information (as source) and the Delta changes that resolved the diagnostic (as target) into a Neural Machine Translation network for training. For the two most prevalent and costly classes of Java compilation errors, namely missing symbols and mismatched methodsignatures, our system called DeepDelta, generates the correct repair changes for 19,314 out of 38,788 (50%) of unseen compilation errors. The correct changes are in the top three suggested axes 86% of the time on average.
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