NDLI: Software fault prediction for object oriented systems:a literature review

Content Provider	ACM Digital Library
Author	Jain, Ankita Malhotra, Ruchika
Abstract	There always has been a demand to produce efficient and high quality software. There are various object oriented metrics that measure various properties of the software like coupling, cohesion, inheritance etc. which affect the software to a large extent. These metrics can be used in predicting important quality attributes such as fault proneness, maintainability, effort, productivity and reliability. Early prediction of fault proneness will help us to focus on testing resources and use them only on the classes which are predicted to be fault-prone. Thus, this will help in early phases of software development to give a measurement of quality assessment. This paper provides the review of the previous studies which are related to software metrics and the fault proneness. In other words, it reviews several journals and conference papers on software fault prediction. There is large number of software metrics proposed in the literature. Each study uses a different subset of these metrics and performs the analysis using different datasets. Also, the researchers have used different approaches such as Support vector machines, naive bayes network, random forest, artificial neural network, decision tree, logistic regression etc. Thus, this study focuses on the metrics used, dataset used and the evaluation or analysis method used by various authors. This review will be beneficial for the future studies as various researchers and practitioners can use it for comparative analysis.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISSN	01635948
DOI	10.1145/2020976.2020991
Journal	ACM SIGSOFT Software Engineering Notes (SOEN)
Volume Number	36
Issue Number	5
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1979-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fault proneness Metrics Empirical validation Object oriented Software quality
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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STATE-OF-THE-ART IN EMPIRICAL VALIDATION OF SOFTWARE METRICS FOR FAULT PRONENESS PREDICTION: SYSTEMATIC REVIEW

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Empirical validation of object-oriented metrics for predicting fault proneness at different severity levels using support vector machines

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Validating the Effectiveness of Object-Oriented Metrics over Multiple Releases for Predicting Fault Proneness

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