Software defect detecting system based on dissymmetrical classified evaluation

A software defect detection system technology, applied in the field of computer systems, can solve problems such as discovery and elimination that consume a lot of manpower and material resources, can not effectively use the discriminant nature, does not take into account the asymmetry of the algorithm, and achieve the effect of improving test performance

Active Publication Date: 2018-05-15
XIAMEN UNIV OF TECH
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Problems solved by technology

[0002] Computer systems include software systems and hardware systems. In the face of large and complex software systems, software defects are frequent problems in computer software. In the entire life cycle of software, software defects become an important factor affecting software quality. Find and troubleshoot software defects is an important task, however, it takes a lot of manpower and material resources to find and eliminate software defects
Although the data acquisition methods of different degree elements are different, different metric elements are not differentiated in the prediction algorithm, but the prediction algorithm is universal. In the prior art, the dictionary learning method is used in software defect detection, but the conventional The dictionary learning algorithm cannot effectively use the discriminative properties of different types of books in the data set, and at the same time, the asymmetry in the algorithm is not considered in the classification evaluation. Therefore, it is imperative to provide a software detection system with effective data processing

Method used

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  • Software defect detecting system based on dissymmetrical classified evaluation
  • Software defect detecting system based on dissymmetrical classified evaluation
  • Software defect detecting system based on dissymmetrical classified evaluation

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Embodiment 1

[0042]A software defect detection system based on asymmetric classification evaluation, the system includes a software data input interface, a controller and a detection result output port, the software data input interface is used to receive software data to be detected, and the controller is used to The software data is detected and the result is output through the detection result output port, and the software data is a software module of the software to be detected.

[0043] The controller is structured to implement the following steps:

[0044] Step (1), detecting the received software module;

[0045] Step (2), obtaining the original software measurement data set, and preprocessing the data of the original software measurement data set;

[0046] Step (3), divide the data into training samples and test samples, define A=[A 1 , A 2 , A 3 ..., A i ,...,A c ] is the training sample set, where A i For the sub-sample set of the i-th class, define X=[X 1 , X 2 , X 3 ....

Embodiment 2

[0076] First, detect the received software module through the interface, obtain the data of the original software measurement data set, and preprocess the data of the original software measurement data set, define A=[A 1 , A 2 , A 3 ..., A i ,...,A c ] is the training sample set, where A i For the sub-sample set of the i-th class, define X=[X 1 , X 2 , X 3 ...,X i ,...,X c ] is the representation coefficient matrix of A about the dictionary set D, and represents the sample set by linear combination:

[0077] A≈DX

[0078] Among them, X i is the i-th class sample A i Representation coefficients obtained by training on the D set.

[0079] Based on the above content, the data is divided into training samples and test samples, which are commonly used technical methods in this field, and will not be repeated here.

[0080] Establish the learning model of the dictionary discriminant item, and its mathematical model is as follows:

[0081] P (D，X) = arg (D,X) min{r(A...

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Abstract

The invention provides a software defect detecting system based on dissymmetrical classified evaluation. The system comprises a software data input interface, a controller and a detection result output port. The controller is used for firstly detecting a received software module, obtaining an original software measurement dataset, preprocessing data of the original software measurement dataset, dividing the data into a training sample and a testing sample, conducting dictionary learning modeling on the data of the training sample, distinguishing and structuralizing dictionaries, using a dissymmetrical classifier to conduct performance evaluation, detecting and shifting the testing sample, using the model to conduct defect detection on the software detection module, feeding an evaluation result back to a tester, and completing the detection; then outputting the detection result to a user through the detection result output port. By means of the software defect detecting system based onthe dissymmetrical classified evaluation, the expression capability of the dictionaries can be strengthened, and the software defect detecting system has great discriminating performance; meanwhile,the errors caused by data imbalance are effectively reduced, and software defects are accurately located.

Description

【Technical field】 [0001] The invention relates to a computer system, in particular to a software defect detection system based on asymmetric classification evaluation. 【Background technique】 [0002] Computer systems include software systems and hardware systems. In the face of large and complex software systems, software defects are frequent problems in computer software. In the entire life cycle of software, software defects become an important factor affecting software quality. Find and troubleshoot software defects It is an important work, however, it takes a lot of manpower and material resources to discover and eliminate software defects. In 2006, the United States spent approximately $78 billion in costs related to software defects. According to the data of the US Department of Defense, the funds spent on software-related work account for about 42% of the entire IT product. 53%-87%. Therefore, reasonable defect prediction can help to find undiscovered but real defe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36
CPCG06F11/3684
Inventor 马樱朱顺痣翁伟王琰
Owner XIAMEN UNIV OF TECH
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