Total views : 423
Utility of an Object Oriented Reusability Metrics and Estimation Complexity
Background/Objectives: In this 21st century, Reusability imparts powerful tools in the software industry. More and less 80% code is reused in the new project. Evaluation of metrics form the software code is now a challenging task as well as how much percentage of code is used from the existing one. This can be achieved by using CK (Chidambaram and Kemmerer Metrics). Methods/Statistical Analysis: There are numerous metrics are defined which distinguish the actual object. The proposed new metrics which is the combinations of one of the CK metrics suite and which calculates the reusable codes in the object oriented programme. Findings: In the inheritance if we take maximum depth of class in the hierarchy then found more chance for reusability of the inherited metrics. So DIT (Depth of Inheritance) has positive sign on the reusability of the class. If reasonable value for number of children then more scope of reuse in the class. If we have more number of methods in the class then more impact will be more on the children class and restrictive the possible of reuse. Conclusion: The OOS (Object Oriented System) using the parameterized constructor in C++ programs is more reusable up to some extent. When we will get the larger ethics (values) of proposed Metrics-2 and - 3 then definitely it gives the negative collision on the reusability. So the constructor having parameters (parameterized Constructor) gives the negative impact on the reusability of the classes.
CK Metrics, Object Oriented Metrics, Reusability Factor
- Li W, Henry S, Kafura D, Schulman R. Measuring object oriented design. Journal of Object Oriented Program. 1995; 8(4):48–55.
- Chidambaram SR, Kemerer CF. A metrics suite for object oriented design. IEEE Transaction. Software Engineering.1994; 20(x):476–93.
- Lorentz M, Kidd J. Object-Oriented software metrics.Prentice Hall object-oriented series. Englewood Cliffs: Pretice Hall; 1994.
- Henderson-sellers B. Object-oriented metrics. Measures of complexity. New Jersey: Prentice Hall; 1996.
- Aggarawal KK, Singh Y, Kaur A, Malhotra R. Empirical study of Object-Oriented metrics. Journal of System Software. 2006; 23:111–22.
- Taylor D. Object-Oriented Technology: A managers Guide.Second Printing. Reading U.S: Addison-Wesley; 1990.
- Aggarawal KK, Singh Y. Software engineering. 2nd ed.Delhi: New Age International Publishers; 2007.
- Harrison R, Counsell SJ, Nithi RV. An evaluation of the MOOD set of Object Oriented Software Metrics. IEEE Transactions on Software Eng.1998; 24(6): 491–6.
- Binder RV. Object-oriented software testing.Communications of the ACM. 1994; 37(9):28–9.
- Purao S, Vaishnavi VK. Product metrics for Object Oriented Systems. ACM Computing Surveys. 2003; 35(2):191–221.
- Lorenz M, Kidd J. Object-oriented software metrics.Englewood Cliffs, New Jersey: Prentice Hall; 1994.
- Dubey SK, Rana A. A comprehensive assessment of ObjectOriented Software Systems using metrics approach. IJCSE.2010; 2(8):2726–30.
- Costagliola G, Ferrucci F, Tortora G, Vitiello G. Class points: An approach for the size estimation of object-oriented systems. IEEE Transactions on Software Eng. 2005; 31(1):52–74.
- Basily VR, Briand LC, Melo WL. A validation of object oriented design metrics as quality indicators. IEEE Transactions on Software Eng. 1996; 22(1): 751–61.
- Babsiya J, Davis CG. A hierarchical model for object oriented design quality assessment. IEEE Transactions on Software Eng. 2002; 28:4–17.
- Briand LC, Wust J. Modelling development effort in object oriented system using design properties. IEEE Transactions on Software Eng. 2001; 27(11):963–86.
- Kim K, Shin Y, Wu C. Complexity measures for objectoriented program based on the entropy. Proc Asia Pacific Software Eng; 1995. p. 127–36.
- Olague HM, Etzkorn LH, Gholston S, Quattlebaum S.Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes. IEEE Transactions on Software Eng. 2007; 33(6):402–19.
- Nair TRG, Selvarani R. Estimation of software reuability: An Engineering Approach, Association for Computing Machinery (ACM)-SIGSOFT, USA. 2010; 35(1).
- PS, Aashima, Kakkar P, Sharma S. A survey on software reusability.IEEE; 2010.
- Aggarwal KK, Singh Y, Kaur A, Malhotra R. Software reuse metrics for object-oriented systems. IEEE; 2005.
- Conte SD, Dunsmore HE, Shen VY. Software engineering metrics and models. Benjamin/Cusmmings; 1986. p. 62–70.
- Singhani H, Suri PR. Testability assessment model for object oriented software based
- on internal and external quality factors. Global Journal of Computer Science and Technology: C Software and Data Engineering. 2015; 15(5).
- Goel BM, Bhatia PK. Analysis of reusability of ObjectOriented System using CK metrics. International Journal of Computer Applications. 2012 Dec; 60(10):32–6. 0975 – 8887.
- Gupta D, Goyal VK, Mittal H. Comparative study of soft computing techniques for software quality model.International Journal of Software Engineering Research and Practices. 2011; 1(1).
- Nuruzzaman M, Hussain A, Tahir HM. Towards increasing web application development productivity through object-oriented framework. International Journal of Future Computer and Communication. 2013 Jun; 2(3).
- Kumar M, Aloysius A. A review on component based software metrics. International Journal of Fuzzy Mathematical.2015; 7(2):185–94.
- Sing PK, Om SP, Sing AP. A framework for assessing the software reusability using fuzzy logic approach for aspect oriented software. I. J. Information Technology and Computer Science. 2015; 02:12–20.
- Gandhi P, Bhatia PK. Reusability metrics for object-oriented system: An alternative approach. IJSE. 2010; 1(4):63–72.
- Patidar K, Gupta RK, Chandel GS. Coupling and cohesion measures in object oriented programming. International Journal of Advanced Research in Computer Science and Software Engineering. 2013 Mar; 3(3).
- Malhotra R, Singh Y, Kaur A. Empirical validation of objectoriented metrics for predicting fault proneness models. Int Journal of Software Quality. 2010. p. 3–33.
- Malhotra E, Khanana M. Investigation of relationship between object-oriented metrics and change proneness. Int Journal of Machine Learning and Cyber. 2013. p. 273–86.
- Maggo S, Gupta C. A machine learning based efficient software reusability prediction model for java based object oriented software. I J Information Technology and Computer Science. 2014; 02:1–13.
- Malhotra R, Jindal R, Jain A. Prediction of defect severity by mining software project reports. Int Journal of System Assurance Engineering and Management. Springer; 2016.
- Malhotra R, Jain A. Fault prediction using statistical and machine learning methods for improving software quality.Int Journal of Information Processing Systems. Korean Journal. 2016; 8(2).
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.