Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function
المؤلفون المشاركون
Ramasamy, Subburaj
Lakshmanan, Indhurani
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-6، 6ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-07-26
دولة النشر
مصر
عدد الصفحات
6
التخصصات الرئيسية
الملخص EN
Reliability is one of the quantifiable software quality attributes.
Software Reliability Growth Models (SRGMs) are used to assess the reliability achieved at different times of testing.
Traditional time-based SRGMs may not be accurate enough in all situations where test effort varies with time.
To overcome this lacuna, test effort was used instead of time in SRGMs.
In the past, finite test effort functions were proposed, which may not be realistic as, at infinite testing time, test effort will be infinite.
Hence in this paper, we propose an infinite test effort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model.
We use Artificial Neural Network (ANN) for training the proposed model with software failure data.
Here it is possible to get a large set of weights for the same model to describe the past failure data equally well.
We use machine learning approach to select the appropriate set of weights for the model which will describe both the past and the future data well.
We compare the performance of the proposed model with existing model using practical software failure data sets.
The proposed log-power TEF based SRGM describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and can be used for software release time determination as well.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ramasamy, Subburaj& Lakshmanan, Indhurani. 2017. Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1192114
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ramasamy, Subburaj& Lakshmanan, Indhurani. Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function. Mathematical Problems in Engineering No. 2017 (2017), pp.1-6.
https://search.emarefa.net/detail/BIM-1192114
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ramasamy, Subburaj& Lakshmanan, Indhurani. Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1192114
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1192114
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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