Study on Agile Process Methodology and Emergence of Unsupervised Learning to Identify Patterns from Object Oriented System

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)

Abstract

Data mining is knowledge extraction for secure software engineering, improves the quality and productivity, poses several challenges, requiring various algorithms to effectively mine text, graph from such database. Fact that building models in the context of the framework one of the task data miners, almost important though all other tasks associated with data mining. Data mining techniques are tackling the right business problem, must understand the data this is available and turn noisy data into data from which we can build robust models. It is important to be aware data mining is really what we might call an agile model. The concept of agility comes from the agile software engineering principles includes increment development of the business requirements and need to check whether the requirement satisfies with the client inputs our testing and rebuilding models improves the performance. For software engineering code execution, code changes list of bugs and requirement engineering our system uses mining techniques to explore valuable data patterns in order to meet better projects inputs and higher quality software systems that delivered on time. Our research uses frequent mining, pattern matching and machine learning applied to agile software architecture model in gathering and also extracting security requirements best effort business rules for novel research.

Keywords

Agile Model Data Mining Software Engineering Architecture & Design Pattern 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dekhtyar, A., Hayes, J.H., Menzies, T.: Text is Software Too. In: Proceedings of the International Workshop on Mining of Software Repositories (MSR), Edinborough, Scotland, pp. 22–27 (May 2004)Google Scholar
  2. 2.
    Hayes, J.H., Dekhtyar, A., Osbourne, J.: Improving Requirements Tracing via Information Retrieval. In: Proceedings of the International Conference on Requirements Engineering (RE), Monterey, California, pp. 151–161 (September 2003)Google Scholar
  3. 3.
    Hayes, J.H., Dekhtyar, A., Sundaram, K.S., Howard, S.: Helping Analysts Trace Requirements: An Objective Look. In: Proceedings of the International Conference on Requirements Engineering (RE), Kyoto, Japan (September 2004)Google Scholar
  4. 4.
    Schwaber, K., Beedle, M.: Agile Software Development with Scrum. Prentice Hall (2001)Google Scholar
  5. 5.
    Beck, K., et al.: The Agile Manifesto (2001), http://www.agilemanifesto.org (downloaded March 6, 2009 )
  6. 6.
    Babchuk, N., Goode, W.J.: Work incentives in a self-determined group. American Sociological Review 16(5), 679–687 (1951)CrossRefGoogle Scholar
  7. 7.
    Badani, M.: Mapping Agile development practices to Traditional PMBOK (June 4, 2001), http://www.pmiissig.Org/pds/DOCS/BadaniBioandAbstractMappingAgileDevelopmentPractices.doc (accessed February 29, 2007)
  8. 8.
    Sliger, M.: Survival guide to going Agile, Rally Software Development Corporation, p. 8 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of CSEBSITHyderabadIndia
  2. 2.Department of CSEGREITHyderabadIndia

Personalised recommendations