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Feature attachment code odor detection method based on deep learning

A technology of deep learning and detection methods, applied in neural learning methods, code refactoring, software maintenance/management, etc., to achieve the effect of improving the average accuracy rate and the average recall rate

Inactive Publication Date: 2018-10-16
BEIJING INSTITUTE OF TECHNOLOGYGY
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AI Technical Summary

Problems solved by technology

However, empirical studies have shown that this statistical machine learning-based approach to odor detection has key limitations that warrant further study

Method used

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  • Feature attachment code odor detection method based on deep learning
  • Feature attachment code odor detection method based on deep learning
  • Feature attachment code odor detection method based on deep learning

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Experimental program
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Embodiment

[0035] This embodiment elaborates in detail the method and effect when the detection method of the present invention is implemented under 7 open source projects.

[0036] Under the hardware environment shown in Table 1, the open source software shown in Table 2 is trained and predicted.

[0037] Table 1: Hardware environment configuration information table

[0038]

[0039] Table 2: Basic information table of open source software

[0040] Number of open source projects

writing language

Item size (LOC)

Average item size (LOC)

7

Java

11,734～444,493

139,742

[0041] From the 7 open source Java projects, the data of one of the open source projects is used as the test data, and the data of the other 6 open source projects are used as the training data.

[0042] A feature-attachment code smell detection method based on deep learning, such as figure 1 shown, including the following steps:

[0043]Step 1: Extract the movable method in...

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Abstract

The invention relates to a feature attachment code odor detection method based on deep learning, and belongs to the technical field of computer software. The method comprises the steps that movable method information and distance of open source software used for training are firstly extracted to generate a training set, the extracted movable method information and distance are converted to training data indicated by word vectors, and the training data is input to a convolutional neural network to train a neural network model; method information and distance of a movable method of the open source software used for testing are extracted to generate a testing data set, and data concentrated in the testing data set is converted to to-be-tested data indicated by the word vectors. The to-be-tested data is input to the neural network model, 0 or 1 is automatically output by the model, wherein 1 represents that feature attachment code odor exists, and 0 represents that the feature attachment code odor does not exist. Compared with existing detecting methods, the method greatly improves average recall rate, and average accuracy is improved.

Description

technical field [0001] The invention relates to a code odor detection method aimed at feature attachment, and belongs to the technical field of computer software. Background technique [0002] Code Refactoring refers to improving the internal structure of a software system without changing its external behavior. Code refactoring can improve the quality and performance of the software by adjusting the program code, make the design pattern and structure of the program more reasonable, and improve the scalability and maintainability of the software. Code refactoring is a methodical approach to program organization that has been honed over time to minimize the probability of errors being introduced during the organization process. Essentially, code refactoring is about improving the design of the code after it is written. Software refactoring is widely used to improve software quality by refactoring each of its structures while keeping its external behavior unchanged. Most ex...

Claims

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Application Information

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IPC IPC(8): G06F8/72G06K9/62G06N3/08
CPCG06F8/72G06N3/08G06F18/2411G06F18/214
Inventor 刘辉许志凤
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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