International Journal of Application or Innovation in Engineering & Management
An Inspiration for Recent Innovation & Research….
ISSN 2319 – 4847
www.ijaiem.org
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Title: |
Software Defect Prediction for Quality Improvement Using Hybrid Approach
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Author Name: |
Pooja Paramshetti , D. A. Phalke |
Abstract: |
ABSTRACT
In today’s computing environment, software systems have become increasingly complex and versatile. Therefore it is necessary
to continuously identify and correct software design defects. Software defect prediction plays an important role in improving
software quality and it helps to reduce time and cost for software testing. The Software defect prediction is a method which
predicts defects based on historical database. Different machine learning techniques are used to predict software defects from
historical databases. The paper mainly focuses on generating accurate rules for software defect prediction System. For this
purpose, K-means clustering technique is used for discretization. Then association rule mining is applied to generate rules in
large volumes of data using Apriori algorithm. Software defect prediction system has been experimented on open sources
NASA defect dataset. It contains software metric data and error data at the function or method level. In this paper comparison
of proposed approach with existing approaches is discussed and the results show that proposed method is generated only
interesting rules therefore it is effective for software defect prediction.
Keywords: Software defect prediction, Software metrics, k-means clustering technique, Apriori algorithm. |
Cite this article: |
Pooja Paramshetti , D. A. Phalke , "
Software Defect Prediction for Quality Improvement Using Hybrid Approach " , International Journal of Application or Innovation in Engineering & Management (IJAIEM),
Volume 4, Issue 6, June 2015 , pp.
099-104 , ISSN 2319 - 4847.
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