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Improved random forest algorithm based system and method for software fault prediction

A software failure and prediction method technology, applied in software testing/debugging, etc., can solve the problems that the prediction model cannot guarantee the prediction performance and the model simplification, etc., and achieve the effect of good performance, high recall rate, and high prediction efficiency.

Active Publication Date: 2013-08-21
XIDIAN UNIV
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Problems solved by technology

[0012] In the above methods except the random forest algorithm, other prediction methods have the problem of overfitting between the training data and the training model; the prediction models of the existing methods cannot guarantee relatively stable prediction performance; the original random forest algorithm cannot guarantee the accuracy of the model. streamline

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  • Improved random forest algorithm based system and method for software fault prediction
  • Improved random forest algorithm based system and method for software fault prediction
  • Improved random forest algorithm based system and method for software fault prediction

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Embodiment Construction

[0035] Such as figure 1 As shown, the fault prediction system of the present invention is composed of the following: a data processing layer, a prediction model construction layer and a fault prediction layer, wherein the data processing layer includes data acquisition and data preprocessing, and the data acquisition utilizes the fault data of the history module through the module The attribute is calculated to obtain an original training data set, and the data preprocessing performs balancing processing on the obtained original training data set to obtain a balanced training data set;

[0036] The prediction model building layer randomly samples the balanced training data set obtained through the preprocessing of the data processing layer, and uses the training data subset obtained after sampling to construct a prediction model and optimize it;

[0037] The fault prediction layer calculates the vector data of the quality attribute set of the system under test, uses the optimi...

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Abstract

The invention discloses improved random forest algorithm based system and method for software fault prediction. The system comprises a data processing layer, a prediction model building layer and a fault predication layer. The method includes calculating a software project attribute set used for acquiring a training model to acquire a training data set of a software prediction model, and performing equalization to the training data set; building a prediction model according to an improved random forest algorithm; screening the model according to performance limiting of accuracy rate and recall ratio; and predicting a software project according to attribute set information of the to-be-predicted software project and a trained prediction model and displaying prediction results and the prediction model. The improved random forest algorithm based system and method for software fault prediction have the advantages of high prediction accuracy rate, performance stability and high execution efficiency, can evaluate whether a final software product reaches specified quality or meets expectation of a user or not, and can guide developers to formulate distribution strategies of software testing and formal verification resources.

Description

technical field [0001] The present invention relates to the field of software engineering quality detection, in particular to a method based on evaluating whether the final software product has reached the specified quality or meeting the expectations of users, or guiding developers to formulate software testing and formal verification resource allocation strategies A system and method for improving software fault prediction of random forest algorithms. Background technique [0002] Software fault prediction technology can help developers find faults in time before software is released, reduce software maintenance costs, and effectively improve software quality. Software metrics and software failure data are often used to build software failure propensity prediction models. The basic principle of software fault prediction is that if the module currently being developed has similar software quality attributes to a faulty module that has been developed before, it means that t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36
Inventor 段振华严蕾田聪张南王小兵罗玲
Owner XIDIAN UNIV
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