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Establishing a software defect prediction model via effective dimension reduction
- Source :
- Information Sciences. 477:399-409
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- With the continued growth of interoperable software developed for Internet of Things (IoT), there is a growing demand to predict software defect at various testing and operational phases. This paper solves the software defect prediction problem by proposing a novel model with the help of a local tangent space alignment support vector machine (LTSA-SVM) algorithm. The model employs the SVM algorithm as the basic classifier of software defect distribution prediction model. Then, the model parameters are optimized by combining a grid search method and ten-fold cross validation. In the traditional dimensionality reduction algorithms, data loss caused by the poor attributes of data nonlinearity reduces the accuracy of SVM. Aiming at this problem, this paper uses a LTSA algorithm to extract the intrinsic structure of low-dimensional data and performs effective dimension reduction. The SVM algorithm is trained by the reduced dimension data. Finally, the feasibility of the prediction model is verified. Compared with the single SVM and the LLE-SVM prediction algorithm, the prediction model in this paper improves the prediction accuracy and F-measure by 1–4%.
- Subjects :
- Information Systems and Management
Computer science
02 engineering and technology
computer.software_genre
Cross-validation
Theoretical Computer Science
Software
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
business.industry
Dimensionality reduction
05 social sciences
050301 education
Effective dimension
Computer Science Applications
Support vector machine
Software bug
Control and Systems Engineering
Hyperparameter optimization
Local tangent space alignment
020201 artificial intelligence & image processing
Data mining
business
0503 education
computer
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 477
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........18559e08092442d665e6012b13e8ec97