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Representing code changes as numeric feature vectors, i.e., code changerepresentations, is usually an essential step to automate many softwareengineering tasks related to code changes, e.g., commit message generation andjust-in-time defect prediction. Intuitively, the quality of code changerepresentations is crucial for the effectiveness of automated approaches. Priorwork on code changes usually designs and evaluates code change representationapproaches for a specific task, and little work has investigated code changeencoders that can be used and jointly trained on various tasks. To fill thisgap, this work proposes a novel Code Change Representation learning approachnamed CCRep, which can learn to encode code changes as feature vectors fordiverse downstream tasks. Specifically, CCRep regards a code change as thecombination of its before-change and after-change code, leverages a pre-trainedcode model to obtain high-quality contextual embeddings of code, and uses anovel mechanism named query back to extract and encode the changed codefragments and make them explicitly interact with the whole code change. Toevaluate CCRep and demonstrate its applicability to diverse code-change-relatedtasks, we apply it to three tasks: commit message generation, patch correctnessassessment, and just-in-time defect prediction. Experimental results show thatCCRep outperforms the state-of-the-art techniques on each task.

CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back