The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Methodology for Model-Based Reliability Estimation
Abstract
In recent years, reliability assessment is an essential process in system quality assessments. However, the best practice of software engineering for reliability analysis is not yet of its matured stage. The existing works are only capable to explicitly apply a small portion of reliability analysis in a standard software development process. In addition, an existing reliability assessment is based on an assumption provided by domain experts. This assumption is often exposed to errors. An effective reliability assessment should be based on reliability requirements that could be quantitatively estimated using metrics. The reliability requirements can be visualized using reliability model. However, existing reliability models are not expressive enough and do not provide consistence-modeling mechanism to allow developers to estimate reliability parameter values. Consequently, the reliability estimation using those parameters is usually oversimplified. With this situation, the inconsistency problem could happen between different estimation stages. In this chapter, a new Model-Based Reliability Estimation (MBRE) methodology is developed. The methodology consists of reliability model and reliability estimation model. The methodology provides a systematic way to estimate system reliability, emphasizing the reliability model for producing reliability parameters which will be used by the reliability estimation model. These models are built upon the timing properties, which is the primary input value for reliability assessment.
Related Content
|
Preethi, Sapna R., Mohammed Mujeer Ulla.
© 2023.
16 pages.
|
|
Srividya P..
© 2023.
12 pages.
|
|
Preeti Sahu.
© 2023.
15 pages.
|
|
Vandana Niranjan.
© 2023.
23 pages.
|
|
S. Darwin, E. Fantin Irudaya Raj, M. Appadurai, M. Chithambara Thanu.
© 2023.
33 pages.
|
|
Shankara Murthy H. M., Niranjana Rai, Ramakrishna N. Hegde.
© 2023.
23 pages.
|
|
Jothimani K., Bhagya Jyothi K. L..
© 2023.
19 pages.
|
|
|