Earticle

초록

영어

A challenging part of software testing entails the generation of test cases. A good test case should have the quality to cover more features of test objective. The techniques for automatic generation of test cases try to efficiently find a small set of cases that allow an adequacy criterion to be fulfilled, thus reducing the cost of software testing and resulting in more efficient testing of software products. In this paper we introduce an all-around classification framework for automatic test case generation approaches in terms of test type and Algorithm, and represent some test case evaluation approaches. Finally we illustrate a comparison between different existing techniques.

목차

Abstract
 1. Introduction
 2. Overview
 3. Automatic Test Case Generation
 4. Proposed Classification Framework in Terms of Test Type
 5. Proposed Classification Framework in Terms of Algorithm
  5.1. Random-based Automatic Test Case Generation
  5.2. Search-based Automatic Test Case Generation
  5.3. Data Mining-based Automatic Test Case Generation
 6. Classification of Test Case Generation Evaluation Approaches
  6.1. Number of Faults Detected (mutation testing):
  6.2. Coverage Criteria:
  6.3. Input/output Analysis
  6.4. Data Mining Approaches:
 8. Discussions
 7. Conclusion
 Acknowledgements
 References

키워드

저자

• Mohammad Reza Keyvanpour [ Computer engineering department, Alzahra University ]
• Hajar Homayouni [ Computer engineering department, Alzahra University ]
• Hossein Shirazee [ Computer engineering department, Islamic Azad University ]

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