NDLI: Investigating object-oriented design metrics to predict fault-proneness of software modules

Content Provider	IEEE Xplore Digital Library
Author	Rathore, S.S. Gupta, A.
Copyright Year	2012
Description	Author affiliation: Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India (Rathore, S.S.; Gupta, A.)
Abstract	This paper empirically investigates the relationship of class design level object-oriented metrics with fault proneness of object-oriented software system. The aim of this study is to evaluate the capability of the design attributes related to coupling, cohesion, complexity, inheritance and size with their corresponding metrics in predicting fault proneness both in independent and combine basis. In this paper, we conducted two set of systematic investigations using publicly available project datasets over its multiple subsequent releases to performed our investigation and four machine learning techniques to validated our results. The first set of investigation consisted of applying the univariate logistic regression (ULR), Spearman's correlation and AUC (Area under ROC curve) analysis on four PROMISE datasets. This investigation evaluated the capability of each metric to predict fault proneness, when used in isolation. The second set of experiments consisted of applying the four machine learning techniques on the next two subsequent versions of the same project datasets to validate the effectiveness of the metrics. Based on the results of individual performance of metrics, we used only those metrics that are found significant, to build multivariate prediction models. Next, we evaluated the significant metrics related to design attributes both in isolation and in combination to validated their capability of predicting fault proneness. Our results suggested that models built on coupling and complexity metrics are better and more accurate than those built on using the rest of metrics.
Starting Page	1
Ending Page	10
File Size	265329
Page Count	10
File Format	PDF
ISBN	9781467321747
e-ISBN	9781467321778
DOI	10.1109/CONSEG.2012.6349484
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-05
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Predictive models Object oriented modeling Couplings Complexity theory Software Correlation design attributes Object-oriented metrics empirical validation fault prediction machine learning techniques
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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