On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations (2008.01566v3)

Abstract: With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analysis techniques. Although such neural program models have been tested on various existing datasets, the extent to which they generalize to unforeseen source code is largely unknown. Since it is very challenging to test neural program models on all unforeseen programs, in this paper, we propose to evaluate the generalizability of neural program models with respect to semantic-preserving transformations: a generalizable neural program model should perform equally well on programs that are of the same semantics but of different lexical appearances and syntactical structures. We compare the results of various neural program models for the method name prediction task on programs before and after automated semantic-preserving transformations. We use three Java datasets of different sizes and three state-of-the-art neural network models for code, namely code2vec, code2seq, and GGNN, to build nine such neural program models for evaluation. Our results show that even with small semantically preserving changes to the programs, these neural program models often fail to generalize their performance. Our results also suggest that neural program models based on data and control dependencies in programs generalize better than neural program models based only on abstract syntax trees. On the positive side, we observe that as the size of the training dataset grows and diversifies the generalizability of correct predictions produced by the neural program models can be improved too. Our results on the generalizability of neural program models provide insights to measure their limitations and provide a stepping stone for their improvement.

• The paper presents a comprehensive review of the elsarticle class to help authors format submissions in compliance with Elsevier’s guidelines.
• The paper outlines key elements such as document structure, flexible author grouping, and effective bibliography management using BibTeX.
• The paper underscores practical benefits in reducing formatting challenges and streamlining the publication process for academic manuscripts.

Overview of the Elsevier LaTeX Template

This paper presents an extensive review and guidance on the use of the elsarticle class, a LaTeX document class provided for authors preparing manuscripts for Elsevier journals. The document serves a dual purpose: first, as a template for academic authors to format their submissions in compliance with Elsevier's specifications, and second, as an instructional guide on the installation and implementation of the template within various TeX environments. The primary focus of this work is to elucidate the functionality, installation, and usage of the elsarticle LaTeX class, ensuring authors can efficiently produce robust and appropriately formatted manuscripts.

Key Elements of the Template

The paper details several aspects of the elsarticle template that are of particular importance for authors:

• Document Structure: It highlights the document class's foundation on the standard article class, while expanding its capabilities to meet Elsevier's requirements. Authors can format various elements such as document style, baselineskip, and front matter efficiently.
• Front Matter Specifications: The paper emphasizes two primary methods for structuring author names and affiliations—grouping authors by affiliation and employing footnotes. This flexibility caters to diverse journal preferences and submission guidelines.
• Bibliography Management: The template provides for various Elsevier-specific bibliography styles and recommends the use of BibTeX for generating bibliographies, with an emphasis on including DOIs where available to enhance citation accuracy and traceability.

Practical Implications

By simplifying the manuscript preparation process, the elsarticle class significantly reduces the formatting burden on researchers, allowing them to focus more on content rather than stylistic compliance. This approach aligns with the broader objective of academic publishing—to disseminate high-quality research efficiently and with minimal barriers. Understanding and applying the instructions contained within this paper ensures compliance with Elsevier’s publication standards, hence facilitating smoother peer review and publication processes.

Theoretical Implications

While the paper itself does not delve into theoretical propositions, its contribution lies in setting a standardized framework that can be adopted by researchers across a multitude of disciplines. The consistency offered by such templates in promoting uniformity across submissions is invaluable for both the authors and editorial staff, reinforcing the integrity and professionalism of the publication process.

Future Speculations

Advancements in AI and machine learning could further simplify and revolutionize the manuscript preparation process. Future developments may include intelligent tools embedded within LaTeX editors that automatically adapt authors' inputs to specific journal guidelines, potentially alleviating the initial learning curve associated with LaTeX for new users. Moreover, with the increasing importance of open access and digital publication formats, templates such as elsarticle might evolve to include more dynamic content capabilities, integrating multimedia elements seamlessly.

In summary, this paper serves as a comprehensive guide for authors using LaTeX to prepare manuscripts for publication in Elsevier journals, offering detailed instructions on both the technical setup and the application of specific formatting standards.