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Related Concept Videos

Introduction to Structures01:30

Introduction to Structures

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A structure is defined as a system of interconnected members designed to support or transfer forces and successfully withstand the loads acting on them. The internal forces of a structure can be determined by decomposing the structure and analyzing the free-body diagrams of the individual members or of a combination of members. This helps in understanding the structural elements' behavior and ensuring that the structure is stable and can withstand the subjected loads.
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A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
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Signal Flow Graphs01:18

Signal Flow Graphs

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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
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Stability of structures01:14

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In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
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Automatic Code Review by Learning the Structure Information of Code Graph.

Ying Yin1, Yuhai Zhao1, Yiming Sun1

  • 1School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

Automated code review models can enhance efficiency. This study introduces a novel approach using program dependency graphs and CodeBERT to improve code review by integrating structural and sequential information, outperforming existing methods.

Keywords:
CodeBERTcode reviewdeep learningprogram dependency graph

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • The increasing volume of software code necessitates efficient automated code review processes.
  • Existing deep learning models for code review primarily rely on code sequence information, neglecting valuable structural and semantic data.
  • Improving the learning of code structure is crucial for more effective automated code review.

Purpose of the Study:

  • To develop an automated code review model that integrates both code structure and sequence information.
  • To enhance the efficiency and accuracy of automated code review processes.
  • To address the limitations of existing models that only consider code sequence information.

Main Methods:

  • A novel program dependency graph serialization algorithm (PDG2Seq) was developed to convert program dependency graphs into lossless code sequences, preserving structural and semantic information.
  • An automated code review model was designed using the pre-trained CodeBERT architecture, fusing program structure and code sequence information.
  • The model was fine-tuned for code review tasks, including automatic code modification.

Main Results:

  • The proposed model demonstrated significant improvements in code review tasks compared to the best existing algorithms (1-encoder/2-encoder).
  • Performance gains were measured using BLEU, Lewinshtein distance, and ROUGE-L metrics, indicating superior accuracy and effectiveness.
  • The integration of program structure information enhanced the model's ability to understand and review code.

Conclusions:

  • The developed automated code review model effectively leverages program structure and code sequence information for improved performance.
  • The PDG2Seq algorithm successfully captures essential code structure and semantic details in a serial format.
  • This approach offers a promising direction for advancing automated code review technology and addressing the challenges of large-scale software development.