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Article Information:
A Comparative Study of Different Software Fault Prediction and Classification Techniques
C.D. Rajaganapathy and A. Subramani
Corresponding Author: C.D. Rajaganapathy
Submitted: February 26, 2015
Accepted: March 25, 2015
Published: July 10, 2015 |
Abstract:
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The main aim of this study is to survey about various techniques of fault prediction, clustering and classification to identify the defects in software modules. A software system consists of various modules and any of these modules can contain the fault that harmfully affects the reliability of the system. But early predictions of faulty modules can help in producing fault free software. So, it is better to classify modules as faulty or non-faulty after completing the coding. Then, more efforts can be put on the faulty modules to produce a reliable software. A fault is a defect or error in a source code that causes failures when executed. A faulty software module is the one containing number of faults, which causes software failure in an executable product. A software module is a set of functionally related source code files based on the system’s architecture. Fault data can be collected from problem reporting system based on the module level. Defect prediction is particularly important in the field of software quality and reliability. Accurate prediction of faulty modules enables the verification and validation activities focused on the critical software components. A software quality classification model predicts the risk factor for software modules, which is an effective tool for targeting timely quality improvement actions. A desired classification technique provides better classification accuracy and robustness. This study surveys various fault prediction, clustering and classification techniques in order to identify the defects in software modules.
Key words: Bayesian classification, Expectation Maximization (EM), Fuzzy C-Means (FCM) clustering, Hyper Quad Tree (HQT), k-means clustering, Similarity-based Software Clustering (SISC), spiral life cycle model, Support Vector classification (SVM)
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Cite this Reference:
C.D. Rajaganapathy and A. Subramani, . A Comparative Study of Different Software Fault Prediction and Classification Techniques. Research Journal of Applied Sciences, Engineering and Technology, (7): 831-840.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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