Tree-to-tree Neural Networks for Program Translation

Abstract: Program translation is an important tool to migrate legacy code in one language into an ecosystem built in a different language. In this work, we are the first to employ deep neural networks toward tackling this problem. We observe that program translation is a modular procedure, in which a sub-tree of the source tree is translated into the corresponding target sub-tree at each step. To capture this intuition, we design a tree-to-tree neural network to translate a source tree into a target one. Meanwhile, we develop an attention mechanism for the tree-to-tree model, so that when the decoder expands one non-terminal in the target tree, the attention mechanism locates the corresponding sub-tree in the source tree to guide the expansion of the decoder. We evaluate the program translation capability of our tree-to-tree model against several state-of-the-art approaches. Compared against other neural translation models, we observe that our approach is consistently better than the baselines with a margin of up to 15 points. Further, our approach can improve the previous state-of-the-art program translation approaches by a margin of 20 points on the translation of real-world projects.

• The paper introduces a novel tree-to-tree neural network architecture specifically designed to capture the hierarchical structure of programming languages for improved translation.
• Empirical results show the proposed tree-to-tree model improves program translation accuracy by up to 15% and achieved a 20% improvement for Java to C# translation compared to baselines.
• The research offers a more effective framework for automated code translation and opens avenues for applying tree-structured models to other hierarchical language processing tasks.

Analysis of "Tree-to-tree Neural Networks for Program Translation"

The paper "Tree-to-tree Neural Networks for Program Translation" delves deeply into employing neural networks to facilitate the translation of programs across different programming languages. Given the necessity of translating legacy code into modern frameworks or other programming languages, this research provides valuable insights into how deep learning can be harnessed to automate and potentially improve the accuracy and efficiency of program translations, which traditionally have been a labor-intensive manual process.

The researchers introduce a novel tree-to-tree neural network architecture, explicitly designed to accommodate the hierarchical nature of programming languages. This model uses the inherent tree structure of programming languages to improve translation accuracy and overcome the limitations of traditional sequence-to-sequence models, which struggle with the rigorous syntactical constraints of code.

Key Contributions

1. Tree-to-Tree Neural Network Architecture: The essential contribution of this work lies in developing a tree-to-tree architecture that simultaneously captures the structural properties of programming languages in both the source and target domains.
2. Attention Mechanism in Tree Decoding: The inclusion of an attention mechanism assists in maintaining focus on specific sub-components of the source tree while generating corresponding elements in the target tree. This targeted attention is crucial given the strict syntactic requirements of code that demand high translation fidelity.
3. Experimental Results: The empirical evaluation showcases that the proposed approach surpasses existing neural translation methods, exhibiting improvements in program translation accuracy by margins of up to 15% compared to state-of-the-art baselines. For real-world Java to C# translation tasks, the model achieved a 20% accuracy improvement over previous methods.

Implications

The implications of this research are twofold: practical and theoretical. Practically, it introduces a more effective framework for translating code, reducing the potential for human error and improving speed and scalability. Theoretically, it opens up new avenues for utilizing tree-structured models in other hierarchical language processing tasks, potentially extending beyond programming languages to any domain where data is naturally represented in a tree structure.

Future Directions

The paper suggests several challenges and future work areas: handling longer sequence generalization, determining strategies for infinite vocabulary sets, and coping with data scarcity and alignment issues in training datasets. Additionally, adapting this model to a wider variety of tree-based tasks could further validate the versatility and robustness of the approach.

Conclusively, this paper provides a foundational leap in machine learning-driven program translation, demonstrating that leveraging structural representations can significantly boost translation models' efficacy and efficiency. This research could spearhead further innovations in automated code handling, which remain integral to the evolving landscape of software development and maintenance.