Empirical investigation of fault prediction capability of object oriented metrics of open source software

Open source software systems are playing important roles in many scientific and business software applications. To ensure acceptable levels of software quality Open source software (OSS) development process uses advanced and effective techniques. Quality improvement involves the detection of potential relationship between defect and open source software metrics. Many companies are investing in open source projects for making effective software. But, because open source software is often developed with a different management style and groups of people than the industrial ones, the quality and reliability of the code needs to be investigated. Hence, more projects need to be measured to obtain information about the characteristics and nature of the source code. This paper presents an empirical study of the fault prediction capabilities of object-oriented metrics given by Chidamber and Kemerer. We have carried out an empirical study and tried to find whether these metrics are significantly associated with faults or not. For this we have extracted source code processed it for metrics and associated it with the bugs. Finally the fault prediction capabilities of object oriented metrics have been evaluated by using Naïve Bayes and J48 machine learning algorithms.

[1]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[2]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

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

[4]  B. Noble,et al.  On certain integrals of Lipschitz-Hankel type involving products of bessel functions , 1955, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[5]  Pradeep Singh,et al.  An Investigation of the Effect of Discretization on Defect Prediction Using Static Measures , 2009, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.

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

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

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

[9]  Letha H. Etzkorn,et al.  Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes , 2007, IEEE Transactions on Software Engineering.

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

[11]  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.

[12]  Victor R. Basili,et al.  A Validation of Object-Oriented Design Metrics as Quality Indicators , 1996, IEEE Trans. Software Eng..

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

[14]  Shari Lawrence Pfleeger,et al.  Software Metrics : A Rigorous and Practical Approach , 1998 .