Adaboost software defect unbalanced data classification method based on improvement

A data classification and software defect technology, applied in the direction of electrical digital data processing, software testing/debugging, genetic rules, etc., can solve problems such as investing a lot of research and unbalanced data classification

Inactive Publication Date: 2016-06-15
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

However, since there are far fewer defective modules in software modules than non-defective modules, this is also a classification problem for unbalanced data.
At present, the classificat

Method used

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  • Adaboost software defect unbalanced data classification method based on improvement
  • Adaboost software defect unbalanced data classification method based on improvement
  • Adaboost software defect unbalanced data classification method based on improvement

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Embodiment Construction

[0038] Combine below figure 1 The present invention is described in further detail.

[0039] Step 1: First obtain the software feature set and software module data, and perform label processing. Among them, feature set F={f 1 ,f 2 … f m}. Software module data set {X,Y}, X={x 1 ,x 2 …x n}, Y={y 1 ,y 2}={+1,-1}. If software module x i No defects, then (x i ,y i ) = (x i ,-1), on the contrary, (x i ,y i ) = (x i ,+1).

[0040] The second step: use the improved genetic algorithm and BP neural network to select the features of software data, so as to reduce the dimensionality of software features and obtain the optimal feature subset.

[0041] (1) The initial population is randomly generated, and the population size is P (P

[0042] (2) Use the BP neural network to train the data set, and adjust the weight and threshold of the network according to the predi...

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Abstract

The invention discloses an Adaboost software defect unbalanced data classification method based on improvement, and mainly solves the problem that an existing software defect data classification method is poor in classification effect on minority classes. The method comprises the following steps that 1, software data is acquired from a software data set and then preprocessed, software module data is divided into a training set and a testing set for training and testing, and cross validation is performed for ten times; 2, feature selection of the software data is performed by combining a genetic algorithm based on improvement with a BP neural network to obtain an optimal feature subset, and then dimension reduction processing is performed on the software features; 3, the unbalancedness of the software defect data is fully considered, and an Adaboost classifier based on improvement is trained to classify software modules. According to the Adaboost software defect unbalanced data classification method based on improvement, the classification precision of the minority classes can be improved, and the software defect modules can be better detected.

Description

technical field [0001] The invention belongs to the field of software engineering applications, and in particular relates to an improved Adaboost-based software defect imbalance data classification method. Background technique [0002] With the rapid development of contemporary information technology, software systems have been applied to national defense construction, various directions and departments of the national economy, and various fields of human activities. The role played by software systems is increasing, and their size is also increasing accordingly. For example, American Telecom needs a system with more than 100 million lines of code to support; the space shuttle's airborne system has nearly 500,000 lines of code, and the ground control system and processing system has about 350,000 lines of code. After the massive reduction, there are still nearly a million lines of code to operate the entire space system. High stability is extremely important for these equi...

Claims

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Application Information

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IPC IPC(8): G06F11/36G06K9/62G06N3/12
CPCG06F11/3608G06N3/126G06F18/2148G06F18/24
Inventor 李克文邹晶杰
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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