Data Mining Approach in Software Analysis

Data Mining Approach in Software Analysis

  IJCOT-book-cover
 		 International Journal of Computer  & Organization Trends (IJCOT)          
 
© 2011 by IJCOT Journal
Volume-1 Issue-2                          
Year of Publication : 2011
Authors : S.Suyambu Kesavan , Dr.K.Alagarsamy

Citation

S.Suyambu Kesavan , Dr.K.Alagarsamy "Data Mining Approach in Software Analysis", International Journal of Computer & organization Trends (IJCOT), V1(2):11-14 Sep - Oct 2011, ISSN 2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract

Data mining and knowledge discovery have proved to be valuable tools in various domains such as production, health care and management. Data mining also has potential to address some highly challenging areas of software engineering such as adaptability and security. In software engineering process analyst play an important role for gathering information from the statement of user and obtaining the information from many resource. Data mining gives the potential algorithms and resource for collecting the information. In this paper we are merging the concept of data mining algorithms into software engineering techniques to collect the information and produce the better analyst decision

References

[1]A. E. Hassan, A. Mockus, R. C. Holt, and P. M. Johnson.Guest editor’s introduction: Special issue on mining softwarerepositories. IEEE Trans. Softw Eng. , 31(6):426 – 428, 2005.
[2] Josh Eno, Craig W Thompson,” Generating Synthetic Data to Match Data Mining Patterns”, IEEE Internet Computing May/June 2008 pp.78 – 82.
[3]. O.Maqbool, A Karim, H.A.Babri, Misarwar, “Reverse Engineering using Association Rules”, IEEE INMIC 2004, pp. 389 - 395.
[4]. Gang Kou Yipeng, “A Standard for Data Mining based Software Debugging”, IEEE 4 Th e International Conference on NetworkedComputing and advanced Information Management, pp. 149 – 152.
[5]. Ray - Yaung Chang, Andy Podgurski, Jiong Yang, “Discovering Neglected Condition in Software by Mining Dependency Graphs” ,” , IEEETransactions on Software Engineering, Vol. 34, No. 5, September/October 2008, pp. 579 - 596.
[6] Jensen, C. and Scacchi W. Simulating an Automated Approach to Discovery and Modeling ofOpen S ource Software Development Processes. InProceedings of ProSim`03 Workshop on SoftwareProcess Simulation and Modeling, Portland, OR May2003.
[7] D. Harel and S. Maoz. Assert and negate revisited: Modal semantics for umlsequence diagrams. Software and System Modeling , 2007.
[8] D. Lo, S. - C. Khoo, and C. Liu. Efficient mining of iterative patterns forsoftware specification discovery.In KDD , 2007.

Keywords

Software engineering, Data Mining , Clustering algorithm