Python resource sensitive defect code detecting method based on depth neural network

A deep neural network, flawed technology, applied in the computer field to achieve the effect of improving efficiency

Active Publication Date: 2018-04-27
NANJING UNIV
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Problems solved by technology

[0005] In the maintenance phase, developers may fix many of the same defects at the same t

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  • Python resource sensitive defect code detecting method based on depth neural network
  • Python resource sensitive defect code detecting method based on depth neural network
  • Python resource sensitive defect code detecting method based on depth neural network

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Embodiment Construction

[0055] The method of the present invention first collects the repaired source codes of all historical versions of the same Python software through software version control systems such as CVS. Then, perform lexical analysis and syntax analysis on the source code of the historical version and the version to be tested, perform type inference based on the generated abstract syntax tree, mark the variables operated by the resource object, identify the resource code pattern, and repair the information from the history based on the historical repair information. Defective code patterns and safe code patterns are selected from the resource-sensitive code patterns of each version to form relevant pattern pairs and non-correlated pattern pairs. Then, the resource-sensitive code pattern of the version to be tested and the historical defect code pattern are combined into a test pattern pair. Then, according to the extracted pattern features, the similarity of each pattern to each feature...

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Abstract

The invention provides a Python resource sensitive defect code detecting method based on a depth neural network. The Python resource sensitive defect code detecting method comprises the following steps of 1, obtaining a source code of the historical version of the same software and a source code of a to-be-detected version; 2, utilizing type deduction to extract resource sensitive code modes of all versions; 3, extracting relevant characteristics of the resource sensitive code modes; 4, calculating all characteristic similarities between a defect code mode and a safe code mode and between thedefect code mode and a to-be-detected code mode to generate characteristic vectors and obtaining a training set and a testing set; 5, using the training set to train a depth neural network model to combine the characteristics, then using the mode with a testing set to calculate relevance of the depth neural network model and ranking the relevance; 6, conducting prompt, assisting development and maintenance on possibly wrong resource object operations according to a relevance ranking result in a program development and maintenance period. The Python resource sensitive defect code detecting method based on the depth neural network solves the problems that currently, an automated method aiming at Python language resource sensitive code recognition and defect code detection is lack, then the software risk is lowered, the software quality is improved, and thus the efficiency for a developer and a maintainer to develop and maintain software is improved.

Description

technical field [0001] The invention belongs to the field of computer technology, especially the field of software technology, and in particular relates to a method for detecting defect codes of Python resource-sensitive codes based on a deep neural network. Background technique [0002] With the continuous development of software application technology, users have higher and higher requirements for software quality, and software developers are also using various technologies to meet user needs. Resource-sensitive code is a code block or statement that deals with resource objects. In the software development and maintenance phase, many resource-sensitive codes have abnormal hidden dangers, which are often discovered during the maintenance process. With the continuous popularity of agile development techniques and frequent version changes, resource-sensitive codes suddenly trigger exceptions from time to time. The most traditional solution to resource-sensitive code excepti...

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Application Information

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IPC IPC(8): G06F11/36
CPCG06F11/3668
Inventor 陈林潘陶陈芝菲李言辉徐宝文
Owner NANJING UNIV
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