Abstract:
Abstract: The main arena of Software Engineering
development with ood design, development, coding, testing,
implementation, deployment of the software, finally maintaining
the software with good functionality. For the development of
software many organizations are investing more and more budget
in their revenue. Software Engineering development has several
categories of data presented in software engineering such as
Graphical User Interface, Usage graphs, writing text, realities and
images. Significant information be able to be obtained from this
composite data by well recognized data mining techniques such as
association, classification, clustering etc. By discovery hidden
patterns by data mining software engineering data is made illegal.
Software Engineering development has many objectives in
software engineering such as Code and Design optimization,
Project documentation, Development cost estimation etc. Variety
of significant data mining method in each phase of software
development life cycle supports in realizing these objectives
proficiently and the failure rate of software is decreased. . This
paper focused a new hybrid model like combination of Fuzzy
Logic and knowledge management offers a significant method for
developing models for software quality prediction. This research
paper explains about exercise of estimate and valuation at a
particular organization by developments and represents the
outcomes attained with a fuzzy based classification and
knowledge model for the fuzzy knowledge management
predication for the quality of software engineering Approach.
This result illustrate that the significance of Average Error
Evaluation Efficiency observed and used in fuzzy logic is lesser
than Average Error Evaluation Efficiency used in another
regression multiple regression; while the value of prediction is higher value that other prediction models is used before. Thus
Results demonstrate that Hybrid fuzzy knowledge management
predication for the quality of software engineering can be used as
alternative for predicting the Software Development and
Maintenance Quality (SDMQ).
