Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities (1707.04742v2)

Abstract: In the field of automated program repair, the redundancy assumption claims large programs contain the seeds of their own repair. However, most redundancy-based program repair techniques do not reason about the repair ingredients---the code that is reused to craft a patch. We aim to reason about the repair ingredients by using code similarities to prioritize and transform statements in a codebase for patch generation. Our approach, DeepRepair, relies on deep learning to reason about code similarities. Code fragments at well-defined levels of granularity in a codebase can be sorted according to their similarity to suspicious elements (i.e., code elements that contain suspicious statements) and statements can be transformed by mapping out-of-scope identifiers to similar identifiers in scope. We examined these new search strategies for patch generation with respect to effectiveness from the viewpoint of a software maintainer. Our comparative experiments were executed on six open-source Java projects including 374 buggy program revisions and consisted of 19,949 trials spanning 2,616 days of computation time. DeepRepair's search strategy using code similarities generally found compilable ingredients faster than the baseline, jGenProg, but this improvement neither yielded test-adequate patches in fewer attempts (on average) nor found significantly more patches than the baseline. Although the patch counts were not statistically different, there were notable differences between the nature of DeepRepair patches and baseline patches. The results demonstrate that our learning-based approach finds patches that cannot be found by existing redundancy-based repair techniques.

Review of IEEEtran \LaTeX\ Class Documentation

The provided document serves as a detailed guide for authors looking to format their conference papers using the IEEEtran \LaTeX\ class. While the document appears to be more instructional than experimental or theoretical, its relevance lies in its utility for standardizing the formatting of IEEE publications, a critical aspect for researchers submitting to IEEE conferences.

Overview

The guide comprises comprehensive instructions for maintaining consistency in document preparation, ranging from layout specifications to stylistic guidelines. It emphasizes adherence to predefined standards essential for ensuring uniformity across various IEEE conference papers. The document also addresses common pitfalls in \LaTeX\ usage, offering pragmatic solutions to assist authors in the technical preparation of their submissions.

Key Components and Instructions

1. Document Formatting: The template enforces specific formatting features such as defined margins, column widths, and font specifications. Authors are advised against modifying these parameters, ensuring that each paper integrates seamlessly into the larger conference proceedings.
2. Pre-formatting Content: Authors are encouraged to finalize their content before applying the format. Clear instructions are provided for keeping text and graphic files separate initially, followed by careful integration post-formatting, preventing premature layout complexities.
3. Technical Presentation: The guide provides precise instructions on the representation of units and symbols, ensuring dimensional clarity in equations by discouraging mixed unit usage, which may compromise readability and scientific accuracy.
4. Common Mistakes and Recommendations: Extensive coverage is given to typical linguistic and technical errors prevalent in scientific manuscripts. The document places emphasis on correct usage of plurals, homophones, and punctuation, alongside presenting a clear distinction between American and British English conventions.
5. Figure and Table Placement: Considerable attention is given to the placement of figures and tables, instructing authors on the visual balancing of content to enhance the document's professional appearance. Specific guidelines on labeling and captioning aim to optimize clarity and comprehension for readers.

Implications for Research Documentation

While this document does not present new research findings, it serves an integral role in the research dissemination process by providing a reliable framework for standardized paper submission. By facilitating uniformity and readability, it directly impacts the ease with which other researchers can access, scrutinize, and build upon presented work.

Future Considerations

As \LaTeX\ continues to evolve, updates to this guidance will likely be necessary to incorporate newly developed features and address emerging challenges in scientific document preparation. Future iterations might expand on integrating more dynamic elements such as interactive figures or data repositories, reflecting a growing trend toward open and reproducible science.

Although the document does not engage in academic discourse or introduce empirical insights, its significance cannot be overstated in terms of logistical facilitation within the scholarly community. Its pragmatic orientation toward document preparation ensures that the research itself remains the focal point in communications, unobstructed by format-related distractions.