![](/images/graphics-bg.png)
An Enhanced Adaptive Random Testing by Dividing Dimensions Independently
المؤلفون المشاركون
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-10-13
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Random testing (RT) is widely applied in the area of software testing due to its advantages such as simplicity, unbiasedness, and easy implementation.
Adaptive random testing (ART) enhances RT.
It improves the effectiveness of RT by distributing test cases as evenly as possible.
Fixed Size Candidate Set (FSCS) is one of the most well-known ART algorithms.
Its high failure-detection effectiveness only shows at low failure rates in low-dimensional spaces.
In order to solve this problem, the boundary effect of the test case distribution is analyzed, and the FSCS algorithm of a limited candidate set (LCS-FSCS) is proposed.
By utilizing the information gathered from success test cases (no failure-causing test inputs), a tabu generation domain of candidate test case is produced.
This tabu generation domain is eliminated from the current candidate test case generation domain.
Finally, the number of test cases at the boundary is reduced by constraining the candidate test case generation domain.
The boundary effect is effectively relieved, and the distribution of test cases is more even.
The results of the simulation experiment show that the failure-detection effectiveness of LCS-FSCS is significantly improved in high-dimensional spaces.
Meanwhile, the failure-detection effectiveness is also improved for high failure rates and the gap of failure-detection effectiveness between different failure rates is narrowed.
The results of an experiment conducted on some real-life programs show that LCS-FSCS is less effective than FSCS only when the failure distribution is concentrated on the boundary.
In general, the effectiveness of LCS-FSCS is higher than that of FSCS.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Zhibo& Li, Qingbao& Yu, Lei. 2019. An Enhanced Adaptive Random Testing by Dividing Dimensions Independently. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1198071
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Zhibo…[et al.]. An Enhanced Adaptive Random Testing by Dividing Dimensions Independently. Mathematical Problems in Engineering No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1198071
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Zhibo& Li, Qingbao& Yu, Lei. An Enhanced Adaptive Random Testing by Dividing Dimensions Independently. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1198071
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1198071
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)