Bibliographic Details
| Title: |
Performability Analysis Considering Debugging Behaviors for Open Source Solution. |
| Authors: |
YOSHINOBU TAMURA1 tamura@yamaguchi-u.ac.jp, SHIGERU YAMADA2 |
| Source: |
International Journal of Performability Engineering. Jan2013, Vol. 9 Issue 1, p13-21. 9p. |
| Subjects: |
Computer debugging software, Open source software, Stochastic differential equations, Development of application software, Cost control |
| Abstract: |
A large scale open source solution composed of several open source softwares is now attracting attention as the next-generation software development paradigm because of the cost reduction, quick delivery, and work saving. The testing phase of open source solution means the binding testing and system testing. Generally, it makes no sense to perform the unit testing of open source solution, because the unit testing means the test for open source software. Then, it is very important for software managers to assess the binding status of open source solution. In this paper, we propose a new approach to software performability analysis based on stochastic differential equations in order to consider an interesting aspect of the collision status during the binding-phase of open source softwares in open source solution. Especially, we derive several performability assessment measures from our stochastic differential equation model. Also, we analyze actual software fault-count data to show numerical examples of software performability assessment considering the component collision for several open source softwares. Moreover, we show that the proposed performability analysis can assist improvement of quality for the large scale open source solution. [ABSTRACT FROM AUTHOR] |
|
Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |