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A Method for Predicting the Number of Software Defects Based on Feature Selection and Ensemble Learning

A feature selection and software defect technology, applied in software testing/debugging, error detection/correction, electrical digital data processing, etc., can solve problems such as different algorithms, different prediction capabilities, irrelevant regression model performance, etc., to improve accuracy Effect

Inactive Publication Date: 2020-03-10
WUHAN UNIV
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Problems solved by technology

[0008] However, these regression algorithms have different predictive capabilities, and the performance of the algorithms will vary due to different data sets. Experiments have proved that no one algorithm can achieve the best performance in all situations.
Furthermore, the performance of these regression models is still vulnerable to irrelevant, redundant model features

Method used

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  • A Method for Predicting the Number of Software Defects Based on Feature Selection and Ensemble Learning
  • A Method for Predicting the Number of Software Defects Based on Feature Selection and Ensemble Learning
  • A Method for Predicting the Number of Software Defects Based on Feature Selection and Ensemble Learning

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Embodiment Construction

[0046] The flow chart of the method for predicting the number of software defects based on feature selection and integrated learning designed by the present invention is shown in the appendix figure 1 , all steps can be automatically run by those skilled in the art using computer software technology. The specific implementation process of the embodiment is as follows:

[0047] Step 1, mining software historical data, extracting n useful software modules from it. The granularity of software modules can be set as files, packages, classes or functions according to actual application scenarios. Then mark the number of defects in the software module.

[0048] Step 2, extract the attribute feature of software module, for the convenience of setting forth, assume that 5 attribute features are extracted in the embodiment: A 1 , A 2 , A 3 , A 4 , A 5 .

[0049] In this embodiment, the defect data set D={(x 1 ,y 1 ),(x 2 ,y 2 ),(x 3 ,y 3 ),(x 4 ,y 4 ),(x 5 ,y 5 )}, wher...

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Abstract

The invention belongs to the technical field of software defect prediction, and particularly relates to a software defect number predicting method based on feature selection and ensemble learning. For solving the problems that in prediction of the software defect number, irrelevant module features damage the performance of a defect prediction model, regression models have different prediction abilities, an optimal regression algorithm cannot be selected and the like, the method comprises the following steps: filtering irrelevant and redundant features by using a feature selecting method based on a package mode; then using six different regression algorithms including a linear regression algorithm, a ridge regression algorithm, a decision tree regression algorithm, a gradient boosting regression algorithm, a nearest neighbor regression algorithm and a multilayered sensor regression algorithm, an ensemble learning technology is used, and a comprehensive regression model is constructed according to data instances of which features are screened. Compared with a single regression model, the software defect number predicting method based on feature selection and ensemble learning has the characteristic that the accuracy of software defect number prediction is improved.

Description

technical field [0001] The invention belongs to the technical field of software defect prediction, in particular to a method for predicting the number of software defects based on feature selection and integrated learning. Background technique [0002] (1) Software defect number prediction technology [0003] Software has become an important factor affecting national economy, military affairs, politics and even social life. Highly reliable and complex software systems depend on the reliability of the software they employ. Software defects are the potential source of related system errors, failures, crashes, and even machine crashes. The so-called defect, so far, there are many related terms and definitions in academia and industry, such as failure, defect, bug, error, error, failure, failure, etc. According to ISO 9000, the definition of a defect is: to meet the requirements related to the intended or specified use. A defect is a part of the software that already exists a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F11/36
CPCG06F11/3608
Inventor 余啸刘进井溢洋崔晓晖邱昌
Owner WUHAN UNIV
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