On Learning Meaningful Code Changes via Neural Machine Translation (1901.09102v1)

Abstract: Recent years have seen the rise of Deep Learning (DL) techniques applied to source code. Researchers have exploited DL to automate several development and maintenance tasks, such as writing commit messages, generating comments and detecting vulnerabilities among others. One of the long lasting dreams of applying DL to source code is the possibility to automate non-trivial coding activities. While some steps in this direction have been taken (e.g., learning how to fix bugs), there is still a glaring lack of empirical evidence on the types of code changes that can be learned and automatically applied by DL. Our goal is to make this first important step by quantitatively and qualitatively investigating the ability of a Neural Machine Translation (NMT) model to learn how to automatically apply code changes implemented by developers during pull requests. We train and experiment with the NMT model on a set of 236k pairs of code components before and after the implementation of the changes provided in the pull requests. We show that, when applied in a narrow enough context (i.e., small/medium-sized pairs of methods before/after the pull request changes), NMT can automatically replicate the changes implemented by developers during pull requests in up to 36% of the cases. Moreover, our qualitative analysis shows that the model is capable of learning and replicating a wide variety of meaningful code changes, especially refactorings and bug-fixing activities. Our results pave the way for novel research in the area of DL on code, such as the automatic learning and applications of refactoring.

Overview of "On Learning Meaningful Code Changes via Neural Machine Translation"

The paper "On Learning Meaningful Code Changes via Neural Machine Translation" explores the potential of leveraging Neural Machine Translation (NMT) models to learn and apply code changes as performed by developers during pull requests. The paper is grounded in the context of modern software development, where deep learning (DL) techniques have been increasingly applied to automate a variety of non-trivial tasks such as bug fixing, code refactoring, and more. Among these, the translation of code changes has been a particularly intriguing area, given the complexity of programming languages and the nuances of developer-intended modifications.

Methodology

To underpin their investigation, the authors mine data from three large Gerrit code review repositories—Android, Google Source, and Ovirt. This dataset includes 239,522 paired code components, delineating the state of each component before and after a pull request. The primary focus is on method-level changes, suitable for the granularity and context necessary for effective NMT operations.

The NMT model employed is an Encoder-Decoder Recurrent Neural Network, enhanced with an attention mechanism to cater to the intricacies of code dependency and syntax. The training process involves converting the pre-pull request code (source) into the post-pull request code (target), allowing the model to learn transformations effectively.

Quantitative Analysis

The authors rigorously evaluate the performance of their model on a test dataset. The results indicate that NMT can successfully predict developer-intended transformations for up to 36% of the cases when utilizing a beam search of ten generated candidates. These findings suggest that NMT models have sufficient capability to automatically classify and replicate practical code transformations within a constrained context of small to medium-sized methods.

Qualitative Insights

In addition to quantitative metrics, a qualitative analysis is conducted to discern the types of code changes learned by the NMT model. The paper organizes these learned changes into a taxonomy with prominent categories such as bug fixing, method refactoring, method interaction modifications, and enhancements in code readability. Examples from this taxonomy include improvements in exception handling, addition of type parameters for generic methods, and syntax simplifications to enhance code readability.

Implications and Future Directions

The implications of this paper are significant for both software maintenance and the development process. The ability of NMT models to learn code transformations at a meaningful level could lead to tools that automatically apply code changes, reducing the tediousness of manual refactoring and even automating aspects of bug fixing. Furthermore, this approach opens the possibility of transfer learning across different software projects and ecosystems, given the demonstration of the model's efficacy on heterogeneous datasets.

The paper highlights a few constraints, such as the focus on method-level transformations and the exclusion of new method implementations. Addressing these could be potential directions for future research, alongside extending the model's application to various programming languages beyond Java.

Conclusion

The paper successfully establishes NMT as a promising approach for automating code transformations in modern software engineering. By showcasing both quantitative and qualitative analyses, it lays the groundwork for further exploration into deep learning's role in automating and improving software development processes. Such advancements could not only enhance developer productivity but also contribute meaningfully to the evolution of automated software engineering practices.