Semi-supervised probabilistic latent semantic analysis based software change log classification method

A semantic analysis, semi-supervised technology, applied in instrumentation, computing, electrical digital data processing, etc., can solve problems such as error log analysis results

Active Publication Date: 2014-08-13
CHONGQING UNIV
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Problems solved by technology

These misclassification phenomena are likely to lead to log managers getting wrong software change log analysis results

Method used

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  • Semi-supervised probabilistic latent semantic analysis based software change log classification method
  • Semi-supervised probabilistic latent semantic analysis based software change log classification method
  • Semi-supervised probabilistic latent semantic analysis based software change log classification method

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Embodiment Construction

[0062] In the existing software change log classification methods, the weight value assigned to a certain word in a certain change log category is artificially set according to the word frequency characteristics, and the software change log is classified accordingly, so that when synonyms, In the case of polysemous words, misclassification is prone to occur, which reduces the classification accuracy of software change logs and affects the analysis of software change logs by log managers. To solve this problem, the present invention provides a software change log classification method based on semi-supervised probabilistic latent semantic analysis, which combines the word dictionary determined by prior knowledge, and according to the probability correlation between words, The probability correlation between words and change log categories, as well as the probability correlation between software change log itself and change log categories, objectively classify software change log...

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Abstract

The invention provides a semi-supervised probabilistic latent semantic analysis based software change log classification method. A word dictionary determined through prior knowledge is combined, classification is performed on software change logs objectively according to probabilistic dependencies between words, probabilistic dependencies between the words and change log categories and probabilistic dependencies between the software change logs and the change log categories, and accordingly the classification on the software change logs according to weight values of the word frequency characteristics is avoided, the accuracy of the classification can be improved, and the problems that errors are produced and the accuracy is low in the process of the classification on the software change logs due to the fact that the weight values are set artificially in the prior art are effectively solved.

Description

technical field [0001] The invention belongs to the technical fields of computer information technology and software engineering, and in particular relates to a software change log classification method based on semi-supervised probability latent semantic analysis. Background technique [0002] At present, in the computer field, the processed operations are usually recorded, and a processing log is generated, which can be used to understand the operations that have been performed from the recorded processing log, and can also determine the corresponding follow-up based on the recorded processing log. operating strategy. [0003] During the operation, management and maintenance of computer software, it is often necessary to repair the software due to the existence of bugs, errors or defects in the software, or to add software functions or features to the software in order to adapt to new environments or new requirements, or In order to improve the readability, reusability, a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/353
Inventor 张小洪鄢萌傅颖徐玲杨梦宁洪明坚葛永新杨丹
Owner CHONGQING UNIV
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