Probability-Based Unsupervised Defect Prediction Method

A prediction method, an unsupervised technology, applied in error detection/correction, software testing/debugging, instrumentation, etc., can solve problems such as threshold sensitivity and information loss, and achieve the effect of reducing sensitivity, improving performance, and avoiding information loss

Active Publication Date: 2018-07-10
重庆优霓空科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the clustering process of this method compares the software metric element value with its threshold value, the judgment result is sensitive to the threshold value, and there is a problem of information loss

Method used

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  • Probability-Based Unsupervised Defect Prediction Method
  • Probability-Based Unsupervised Defect Prediction Method
  • Probability-Based Unsupervised Defect Prediction Method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0066] S1: Obtain the metric threshold;

[0067] S1a: Obtain the source code of the target software, and obtain the measurement element value of the target software source code; the values ​​of all measurement elements of the target software source code form a set X:

[0068] Specifically, as shown in Table 1, in Table 1, I=7, J=7, that is, the target software includes seven files, and the values ​​of seven metric elements of the source code in these seven files are obtained;

[0069] Table 1

[0070] x i,j

j＝1

j＝2

j＝3

j＝4

j＝5

j＝6

j＝7

i＝1

3

1

3

0

5

1

9

i＝2

1

1

2

0

7

3

8

i＝3

2

3

2

5

5

2

1

i＝4

0

0

8

1

0

1

9

i＝5

1

0

2

5

6

10

8

i＝6

1

4

1

1

7

1

1

i＝7

1

0

1

0

0

1

7

[0071] S1b: Take the median of the metric values ​​of each metric on all files as the metric thr...

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Abstract

The invention relates to a non-supervision defect prediction method based on probabilities. The non-supervision defect prediction method based on probabilities comprises the following steps that firstly, metric unit threshold values are acquired, wherein a median of metric unit values of source codes of each metric serves as a threshold value; secondly, difference values of the metric unit values and the threshold values are subjected to randomization; thirdly, clustering is carried out, wherein the sum of the probabilities of files under all metric units is calculated, and the files with the same values are classified to the same kind; fourthly, if the probability sum corresponding to the some kind of files is larger than or equal to L, the files are marked to be defective, if not, the files are marked to be not defective, and therefore all kinds of files are marked to be a defective kind and a non-defective kind. The possibility of defects of the kinds is represented through the probabilities, the different probabilities are obtained for the different metric units, and the information of the possibilites of defects of the kinds is remained. In the process of marking, an appropriate critical value is selected to carry out marking according to the distribution character of the data concentration defects. While information losses are avoided, the appropriate marking critical value is selected, and the performance of defect prediction is improved.

Description

technical field [0001] The invention relates to the technical field of software defect prediction, in particular to a probability-based unsupervised defect prediction method. Background technique [0002] With the rapid development of the Internet, the application of computer systems in all walks of life has been further expanded, and the maintenance cost of software has received more and more attention. Software defects are the main cause of increased software maintenance costs. In the process of software development, early detection of software defects and completion of modifications can improve software quality and reduce maintenance costs. Software defect prediction refers to the use of source code data in the software development process to predict whether the software has defects. By analyzing the values ​​of different metric elements in the software source code, such as Halstead, McCabe, etc., it can predict the modules, classes or methods that may have defects in t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F11/36
CPCG06F11/3684
Inventor 徐玲陆正发鄢萌杨梦宁葛永新洪明坚张小洪周末杨丹
Owner 重庆优霓空科技有限公司
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