A comparative study of models for predicting fault proneness in object-oriented systems

Demand for quality software has undergone rapid growth during the last few years. This is leading to an increase in development of metrics for measuring the properties of software such as coupling, cohesion or inheritance that can be used in early quality assessments. Quality models that explore the relationship between these properties and quality attributes such as fault proneness, maintainability, effort or productivity are needed to use these metrics effectively. This study reflects the relevance of quality models to industrial practices and the maturity of research in developing these models. In this paper we summarise the results of empirical studies done so far to assess the applicability of fault proneness models across object-oriented software. We perform a systematic study of these to identify general conclusions drawn from them. This work recommends the research methodology that should be followed to predict fault proneness models.

[1]  Ramanath Subramanyam,et al.  Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects , 2003, IEEE Trans. Software Eng..

[2]  Premkumar T. Devanbu,et al.  An Investigation into Coupling Measures for C++ , 1997, Proceedings of the (19th) International Conference on Software Engineering.

[3]  Mark Lorenz.  Object-Oriented Software Metrics , 1994 .

[4]  Michelle Cartwright,et al.  An Empirical Investigation of an Object-Oriented Software System , 2000, IEEE Trans. Software Eng..

[5]  Kamaldeep Kaur,et al.  Alternative Methods to Rank the Impact of Object Oriented Metrics in Fault Prediction Modeling using Neural Networks , 2008 .

[6]  Mei-Hwa Chen,et al.  An empirical study on object-oriented metrics , 1999, Proceedings Sixth International Software Metrics Symposium (Cat. No.PR00403).

[7]  Lionel C. Briand,et al.  A Unified Framework for Coupling Measurement in Object-Oriented Systems , 1999, IEEE Trans. Software Eng..

[8]  Hausi A. Müller,et al.  Predicting fault-proneness using OO metrics. An industrial case study , 2002, Proceedings of the Sixth European Conference on Software Maintenance and Reengineering.

[9]  Yuming Zhou,et al.  Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults , 2006, IEEE Transactions on Software Engineering.

[10]  N Sambasivarao.  Software reuse metrics for object oriented systems , 2007 .

[11]  Chris F. Kemerer,et al.  Towards a metrics suite for object oriented design , 2017, OOPSLA '91.

[12]  Fernando Brito e Abreu,et al.  Evaluating the impact of object-oriented design on software quality , 1996, Proceedings of the 3rd International Software Metrics Symposium.

[13]  Victor R. Basili,et al.  A validation of object oriented metrics as quality indicators , 1996 .

[14]  Walcélio L. Melo,et al.  Polymorphism measures for early risk prediction , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[15]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[16]  Khaled El Emam,et al.  A Validation of Object-oriented Metrics , 1999 .

[17]  G. R. Finnie,et al.  AI tools for software development effort estimation , 1996, Proceedings 1996 International Conference Software Engineering: Education and Practice.

[18]  David P. Tegarden,et al.  A software complexity model of object-oriented systems , 1995, Decis. Support Syst..

[19]  Stephen R. Schach,et al.  Validation of the coupling dependency metric as a predictor of run-time failures and maintenance measures , 1998, Proceedings of the 20th International Conference on Software Engineering.

[20]  Hongyuan Gao,et al.  A simple quantum-inspired bee colony algorithm for discrete optimisation problems , 2013, Int. J. Comput. Appl. Technol..

[21]  Lionel C. Briand,et al.  Exploring the relationships between design measures and software quality in object-oriented systems , 2000, J. Syst. Softw..

[22]  Rachel Harrison,et al.  Experimental assessment of the effect of inheritance on the maintainability of object-oriented systems , 2000, J. Syst. Softw..

[23]  Tibor Gyimóthy,et al.  Empirical validation of object-oriented metrics on open source software for fault prediction , 2005, IEEE Transactions on Software Engineering.

[24]  Chris F. Kemerer,et al.  A Metrics Suite for Object Oriented Design , 2015, IEEE Trans. Software Eng..

[25]  David A. Belsley,et al.  Regression Analysis and its Application: A Data-Oriented Approach.@@@Applied Linear Regression.@@@Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1981 .

[26]  Curtis R. Cook,et al.  Use of Factor Analysis to Develop OOP Software Complexity Metrics , 1994 .

[27]  Lionel C. Briand,et al.  Replicated Case Studies for Investigating Quality Factors in Object-Oriented Designs , 2001, Empirical Software Engineering.

[28]  Lionel C. Briand,et al.  A Unified Framework for Cohesion Measurement in Object-Oriented Systems , 1997, Proceedings Fourth International Software Metrics Symposium.

[29]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[30]  Arvinder Kaur,et al.  Comparative analysis of regression and machine learning methods for predicting fault proneness models , 2009, Int. J. Comput. Appl. Technol..

[31]  James M. Bieman,et al.  Cohesion and reuse in an object-oriented system , 1995, SSR '95.

[32]  Pearl Brereton,et al.  Lessons from applying the systematic literature review process within the software engineering domain , 2007, J. Syst. Softw..

[33]  Javam C. Machado,et al.  The prediction of faulty classes using object-oriented design metrics , 2001, J. Syst. Softw..

[34]  Khaled El Emam,et al.  The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics , 2001, IEEE Trans. Software Eng..

[35]  Lionel C. Briand,et al.  Empirical Studies of Quality Models in Object-Oriented Systems , 2002, Adv. Comput..

[36]  Jianchao Zeng,et al.  An improved decision-making rule of Dempster-Shafer theory application on fault diagnosis system , 2013, Int. J. Comput. Appl. Technol..

[37]  Richard H. Carver,et al.  An Evaluation of the MOOD Set of Object-Oriented Software Metrics , 1998, IEEE Trans. Software Eng..

[38]  D. I. George Amalarethinam,et al.  Analysis of Object Oriented Metrics on a Java Application , 2015 .

[39]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[40]  Sandro Morasca,et al.  Deriving models of software fault-proneness , 2002, SEKE '02.

[41]  C. R. Kothari,et al.  Research Methodology: Methods and Techniques , 2009 .

[42]  Martin Hitz,et al.  Measuring coupling and cohesion in object-oriented systems , 1995 .

[43]  Sallie M. Henry,et al.  Object-oriented metrics that predict maintainability , 1993, J. Syst. Softw..

[44]  Jingsong He,et al.  Swarm intelligence: making differences in analogue circuits structure for fault-tolerance , 2013, Int. J. Comput. Appl. Technol..

[45]  David P. Darcy,et al.  Managerial Use of Metrics for Object-Oriented Software: An Exploratory Analysis , 1998, IEEE Trans. Software Eng..

[46]  N. Madhavji,et al.  Validating Object-Oriented Design Metrics on a Commercial Java Application , 2000 .

[47]  Valery Buzungu,et al.  Predicting Fault-prone Components in a Java Legacy System , 2006 .

[48]  Rachel Harrison,et al.  Coupling metrics for object-oriented design , 1998, Proceedings Fifth International Software Metrics Symposium. Metrics (Cat. No.98TB100262).

[49]  Khaled El Emam,et al.  The Optimal Class Size for Object-Oriented Software , 2002, IEEE Trans. Software Eng..

[50]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[51]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[52]  Brian Henderson-Sellers,et al.  Object-oriented metrics: measures of complexity , 1995 .

[53]  Lionel C. Briand,et al.  Assessing the Applicability of Fault-Proneness Models Across Object-Oriented Software Projects , 2002, IEEE Trans. Software Eng..