Software defect prediction model based on improved LLE-SVM
- Resource Type
- Conference
- Authors
- Shan, Chun; Zhu, Hongjin; Hu, Changzhen; Jing Cui; Xue, Jingfeng
- Source
- 2015 4th International Conference on Computer Science and Network Technology (ICCSNT) Computer Science and Network Technology (ICCSNT), 2015 4th International Conference on. 01:530-535 Dec, 2015
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Software
Support vector machines
Predictive models
Data models
Prediction algorithms
Classification algorithms
Software algorithms
Software defect prediction
local linear embedding
support vector machine
software security
- Language
A recent study namely software defect prediction model based on Local Linear Embedding and Support Vector Machines (LLE-SVM) has indicated that Support Vector Regression (SVR) has an interesting potential in the field of software defect prediction. However, the parameters optimization of LLE-SVM model is computationally expensive by using the grid search algorithm, resulting in a lower efficiency of the model; and it ignores the imbalance of data sets when using SVM classier to differentiate the defective class and non-defective class. Thus resulting in a lower prediction accuracy. To solve these problems in LLE-SVM model, we propose a new software defect prediction model based on the improved Locally Linear Embedding and Support Vector Machines (ILLE-SVM). ILLE-SVM model employed the coarse-to-fine grid search algorithm to search the optimal parameters. It ensured a high accuracy of the parameters and reduced the parameters optimizing time by gradually narrowing the search scope and enlarging the parameters step. As for the question that SVM suffers a performance bias in classification when data sets are unbalanced, we employed gird search algorithm to automatically set the reasonable weights of different class. The comparison between LLE-SVM model and ILLE-SVM model is experimentally verified on four NASA defect data sets. The results indicate that ILLE-SVM model can search the optimal parameters faster than LLE-SVM model and perform better than LLE-SVM in software defect prediction.