A Hybrid eBusiness Software Metrics Framework for Decision Making in Cloud Computing Environment
Autor: | Yajun Zhu, Guodong Nian, Laurence T. Yang, Hai Jin, Feng Zhao |
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Rok vydání: | 2017 |
Předmět: |
Computer Networks and Communications
business.industry Computer science Software development Information technology 020207 software engineering Cloud computing Product metric 02 engineering and technology Software metric Computer Science Applications Data modeling Software Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Systems engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering business Software engineering Information Systems |
Zdroj: | IEEE Systems Journal. 11:1049-1059 |
ISSN: | 2373-7816 1932-8184 |
Popis: | Developing high-quality software is essential for eBusiness organizations to cope with drastic market competition. With the development of cloud computing technologies, eBusiness systems and applications pay more attention to open endedness. In a cloud computing environment, eBusiness systems have the ability to provide information technology resources on demand. Traditional software metric methods in distributed systems and applications are technical and project driven, making the market demand and internal practical operation not perfectly balanced within a cloud-computing-based eBusiness corporation. To address this issue, this paper presents a hybrid framework based on the goal/question/metric paradigm to evaluate the quality and efficiency of previous software products, projects, and development organizations in a cloud computing environment. In our approach, to support decision making at the project and organization levels, three angular metrics are used, i.e., project metrics, product metrics, and organization metrics. Furthermore, an improved radial-basis-function -based model is also provided to manage existing projects and design new projects. Experimental results on a well-known eBusiness organization show that the proposed framework is effective, efficient, and operational. Moreover, using the described decision-making algorithm, the predicted data are very close to actual results on the software cost, the fault rate, the development workload, etc., which are greatly helpful in achieving high-quality software. |
Databáze: | OpenAIRE |
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