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mROSE: To Determine Tool Selection and to Understand Model-Driven Software Evolution
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 42 - Number 1 |
| Year of Publication: 2012 |
| Authors: Madhavi Karanam, Ananda Rao Akepogu |
10.5120/5661-7557
|
Madhavi Karanam, Ananda Rao Akepogu . mROSE: To Determine Tool Selection and to Understand Model-Driven Software Evolution. International Journal of Computer Applications. 42, 1 ( March 2012), 46-55. DOI=10.5120/5661-7557
Abstract
Growing interest in the model driven approaches has largely increased the number of tools into the model driven development environment. Previous research has shown that the stakeholders often do not use or know all of the tools available in the model evolution environment that they regularly use. The common solution to this problem is to provide a means to search through passive help documents. However, this approach requires a stakeholder to be able to express their desires in a form understood by search engine. So, choosing the right tool for MoDSE tasks has become difficult because of the diverse nature of numerous tools available. To overcome this limitation, this paper aims to present a prototypical recommendation system, named mROSE, to provide timely and useful recommendations to stakeholders. Two empirical studies were conducted to investigate if mROSE helps or hinders the stakeholders in MoDSE, if so under what conditions. First one was longitudinal user study and the second one was a laboratory user study. Performance of mROSE was also evaluated by using some of the existing metrics. These studies confirmed that mROSE can help stakeholders to choose right tools more efficiently and users liked the idea of having a recommendation system for MoDSE environment, like mROSE. These studies also revealed future directions that would improve the functionality of mROSE.
References
- van Deursen, Eelco Visser, and Jos Warmer. Model-Driven Software Evolution: A Research Agenda",In Dalila Tamzalit (Eds. ). Proceedings 1st International Workshop on Model-Driven Software Evolution, University of Nantes, 2007. pp. 41-49.
- Michel Hoste, Jorge Pinna Puissant, Tom mens,2008. Challenges In Model_ Driven Software Evolution, Technical report of BENEVOL Workshop, Technische University Eindhoven.
- Martin Robillard, Robert J. Walker, Thomas Zimmermann. Recommendation Systems for Software Engineering. IEEE Software, Vol. 27, No. 4, pp 80-86, July/Aug 2010.
- A. Anand Rao, K. Madhavi, 2010. A Framework for Visualizing Model-Driven Software Evolution- Its Application, International Journal of Computer Science Issues (IJCSI), Vol. 7,Issue 1, No. 3, pp 47-53.
- K. Madhavi, A. Anand Rao,2009. A Framework for Visualizing Model-Driven Software Evolution, Proceedings of IEEE International Advance Computing Conference, Patiala, Punjab, India, pp 1785-1790.
- Rational Product Support for MDA, Model Driven Architecture(MDA) Information Center, IBM,
- modelbased. net, www. modelbased. Net
- Objects by Design Forum, Jelsoft Enterprises Limited. http://forums. objectsbydesign. com
- Behrouz H. Far, Mari Ohmori, Takeshi Baba, Yasukiyo Yamasaki, Zenya Koono, 1996. Merging CASE tools with knowkedge-based technology for automatic software design, Decision Support Systems, Volume 18, issue 1, September, pp 73-82.
- Mikko Konito, 2005. Architectural manifesto:choosing MDA tools, Three categories for evaluation, Model Driven Architecture(MDA) Information Center, IBM,
- Peter Wittmann. Comparison of MDA tools. www. wittmannclan. com
- Philip Liew, Kostas Kontogiannis, Tack Tong, 2004. A Framework for Business Model Driven Development, Proceedings of the International Workshop on Software Technology and Engineering Practice.
- Jordi Cabot, Ernest Teniente, 2006. Constraint Support in MDA tools: A Survey, Proceedings of 2nd European Conference on Model Driven Architecture, LNCS, pp 256-267.
- R. Holmes, R. J. Walker, and G. C. Murphy. Approxi¬mate Structural Context Matching: An Approach for Recommending Relevant Examples. IEEE Trans. Software Eng. , vol. 32, no. 1, 2006, pp. 952–970.
- A. Ankolekar et al. Supporting Online Problem-Solving Communities with the Semantic Web. Proc. Int'l Conf. World Wide Web, ACM Press, 2006, pp. 575–584.
- A. Mockus and J. D. Herbsleb,. Expertise Browser: A Quantitative Approach to Identifying Expertise. Proc. Int'l Conf. Software Eng. (ICSE 02), IEEE CS Press, 2002, pp. 503–512
- S. Thummalapenta and T. Xie, PARSEWeb: A Programming Assistant for Reusing Open Source Code on the Web. Proc. IEEE/ACM Int'l Conf. Automated Software Eng. (ASE 07), ACM Press, 2007, pp. 204–213. RSSE community website at http://rsse. org.
- Emmanouil Vozalis, Konstantinos G. Margaritis,. Analysis of Recommender Systems' Algorithms. In Proceedings of the Ninth Panhellenic Conference in Informatics,2003.
- Mulund Deshpande and Geroge karypis. Item-Based Top-N Recommendation Algorithms, ACM Transactions on information system, Vol. 22, No. 1, January 2004, Pages 143-177.
- Tamer Elsaved, Jimmy Lin and Douglas W. Oard. Pairwise Documents Similarity in Large Collections with MapReduce. Proceedings of ACL-08:HLT, pp 265-268.
- Jeffrey Dean and Sanjay Ghemawat, MapReduce: Simplified Data Processing on Large Clusters. OSDI'04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December, 2004.
- Marcel Bruch, Thorsten Schafer, and mira Mezini. On Evaluating Recommender Systems for API sages. RSSE '08, November 10, Atlanta, Georgia, USA pp16-29.
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