CC2Vec: Distributed Representations of Code Changes (2003.05620v1)

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.

• The paper proposes a neural architecture that learns distributed representations of code changes using associated log messages.
• It employs a hierarchical attention network and multiple comparison operators to capture nuanced differences between added and removed code.
• Empirical results show significant performance gains in log message generation, bug patch identification, and just-in-time defect prediction.

Distributed Representations of Code Changes with CC2Vec

The paper proposes CC2Vec, an innovative neural network architecture designed to learn distributed representations of code changes using the corresponding log messages. By identifying the semantic intent communicated in log messages, CC2Vec enhances the representation of software patches beyond traditional, manually crafted features. This learning strategy is crucial for tasks such as log message generation, bug fixing patch identification, and just-in-time defect prediction.

Key Methodological Advances

At the core of CC2Vec is its ability to model the hierarchical nature of code changes, integrating attention mechanisms that emphasize meaningful differences between the code removed and added. The framework comprises four core components: preprocessing, input layer, feature extraction layers, and feature fusion with prediction layers. The feature extraction relies on a hierarchical attention network (HAN) that processes the structural elements of code changes, such as hunks and lines, offering a detailed understanding useful for patch identification.

The comparison layer, a significant innovation within CC2Vec, uses multiple comparison functions—neural tensor networks, neural networks, similarity measures, element-wise subtraction, and multiplication—to derive a nuanced representation of the differences between added and removed code. By leveraging a variety of comparison operators, the architecture captures diverse aspects of code modifications.

Numerical Results and Implications

Empirical evaluations demonstrate CC2Vec's superior performance over existing methods. In log message generation, for instance, CC2Vec enhances BLEU scores by a notable margin, indicating better alignment with human-authored messages compared to bag-of-words approaches. For bug patch identification, CC2Vec-integrated models markedly increase accuracy, precision, recall, F1-score, and AUC across extensive Linux kernel datasets. Similarly, in JIT defect prediction, CC2Vec-augmented models achieve substantial gains in AUC metrics on diverse datasets, underscoring its utility in real-world, multi-task scenarios.

Theoretical Insights and Future Directions

Theoretically, CC2Vec contributes an innovative approach to learning from unstructured software patches by effectively utilizing semantic feedback from developers’ log messages. Its architecture, rooted in hierarchical representation and attention mechanisms, provides a promising direction for future explorations in AI-driven software engineering. In terms of future developments, researchers and practitioners can expand on CC2Vec’s design by integrating it into broader toolkits for automatic code refactoring, complex patch analysis, and language-agnostic models, thus broadening its applicability across various programming languages and domains.

While the current paper primarily focuses on tasks related to code changes in software development cycles, extending these concepts into more diverse software engineering challenges could yield significant benefits, both practically and theoretically. Moreover, the paper lays a foundation for further experimentation with semi-supervised learning models as a means to harness unlabeled patch data, potentially increasing the efficiency and accuracy of software maintenance efforts.