An Empirical Study of the OTS Components Selection Strategies in the Software Industry
Master thesis data
Title: An Empirical Study of the OTS Components Selection Strategies in the Software Industry
Master Specialization: Information Systems
Topic Aproval date: 18/02/09
Orientation: research
Student: Freddy Vergara
Thesis advisor(s): Claudia Ayala, Xavier Franch
Contact: cayala at lsi.upc.edu
Thesis Description
Nowadays, Component-Based Software Development (CBSD) is considered the standard way of developing software systems. In particular, the reuse of Off-The-Shelf software (OTS) components available on the Internet is playing a major role in CBSD. However, several challenges must be face to efficiently exploit the use of this technology. One of them is the selection of OTS components to be integrated. Choosing the wrong component could have large or even disastrous effects for a project. Therefore a wide range of research has been performed on the selection of components. Nevertheless, still little empirical data exist on how software companies approach selecting OTS components. The only thing that is clear is that the formal methods, proposed in research literature, do not seem to be in use. Therefore, in order to improve the quality and reliability of the software industry s choice of technologies we first have to get a picture of what is done today. The objective of this study is to understand the key factors that driver or hinder selection of OTS components in the industry. In particular, we will focus on the role of experience, tacit knowledge, social networks, communities, and the use of internet resources. Expected results will be a worth for researchers and practitioners to be able to properly examine possible enhancements in the industrial practice. It is worth to mention that another similar study is currently being performed in Norway, leaded by the Norwegian University of Science and Technology (NTNU). Our objective is to merge and assess data from both studies in order to better understand the OTS selection practices in the industry.
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