Earticle

초록

영어

Predicting the existing defects in software products is one of the considerable issues in software engineering that contributes a lot toward saving time in software production and maintenance process. In fact, finding the desirable models for predicting software defects has nowadays turned into one of the main goals of software engineers. Since intricacies and restrictions of software development are increasing and unwilling consequences such as failure and errors decrease software quality and customer satisfaction, producing error-free software is very difficult and challenging. One of the efficient models in this field is multilayer neural network with proper learning algorithm. Many of the learning algorithms suffer from extra overfitting in the learning datasets. In this article, setting multilayer neural network method was used in order to improve and increase generalization capability of learning algorithm in predicting software defects. In order to solve the existing problems, a new method is proposed by developing new learning methods based on support vector machine principles and using evolutionary algorithms. The proposed method prevents from overfitting issue and maximizes classification margin. Efficiency of the proposed algorithm has been validated against 11 machine learning models and statistical methods within 3 NASA datasets. Results reveal that the proposed algorithm provides higher accuracy and precision compared to the other models.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Overview of Support Vector Machine
 4. Multilayer Perceptron Neural Network
 5. The Hybrid Error Prediction Model
 6. Evaluation Method
  6.1. Dataset
  6.2. Efficiency Measurement Criteria
  6.3. Cross Validation
 7. Empirical Result
 8. Conclusion
 References

키워드

저자

• Mohamad Mahdi Askari [ Department of Computer Science Islamic Azad University, Kerman, Iran ]
• Vahid Khatibi Bardsiri [ Department of Computer Science Islamic Azad University, Kerman, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605