CC2Vec: Distributed Representations of Code Changes

Abstract

Existing work on software patches often use features specific to a single task. These works often rely on manually identified features, and human effort is required to identify these features for each task. In this work, we propose CC2Vec, a neural network model that learns a representation of code changes guided by their accompanying log messages, which represent the semantic intent of the code changes. CC2Vec models the hierarchical structure of a code change with the help of the attention mechanism and uses multiple comparison functions to identify the differences between the removed and added code. To evaluate if CC2Vec can produce a distributed representation of code changes that is general and useful for multiple tasks on software patches, we use the vectors produced by CC2Vec for three tasks: log message generation, bug fixing patch identification, and just-in-time defect prediction. In all tasks, the models using CC2Vec outperform the state-of-the-art techniques.

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Cited by in corpus (13)

• CURE: Code-Aware Neural Machine Translation for Automatic Program Repair
• Commit2Vec: Learning Distributed Representations of Code Changes
• Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning
• Test case prioritization using test case diversification and fault-proneness estimations
• Predictive Mutation Analysis via Natural Language Channel in Source Code
• Defectors: A Large, Diverse Python Dataset for Defect Prediction
• Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
• Feature Sets in Just-in-Time Defect Prediction: An Empirical Evaluation
• On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
• Utilizing Source Code Syntax Patterns to Detect Bug Inducing Commits using Machine Learning Models
• Automated Description Generation for Software Patches
• Encoding Version History Context for Better Code Representation
• Code Revert Prediction with Graph Neural Networks: A Case Study at J.P. Morgan Chase