QUALITY OF SOFTWARE PROJECTS – A CASE STUDY

Abstract

In software projects, many points that are overlooked such as time constraints and human factors are causing great problems in the future. By measuring the quality of software projects, problems that may arise in important parameters such as maintenance-repair, functionality and reliability can be eliminated. In this study, metrics that can be used for measuring maintainability quality attribute within the scope of ISO 9126 Quality Standard are examined. In order to perform the study, 40 open source object-oriented software was selected and code complexity analysis was performed. Values of metric sets such as Chidamber and Kemerer (CK), Lorenz and Kidd (LK) and McCabe's complex Suite were determined by the Understand Code Analysis tool. It was determined whether the obtained values exceeded the threshold values indicated in the literature. Frequencies of metrics passing threshold values were determined for 40 open source object-oriented software projects, and the consistency among the metrics was evaluated using WEKA Machine Learning Software and EXCEL Data Analysis Tool. When the results were evaluated, it was observed that in addition to CK metrics such as WMC, CBO, and RFC, which measure the maintainability quality attribute, NOC (CK), NIM (LK), and the ratio of comment/code metrics have been observed to yield significant measurement results.

Keywords

• software quality,maintainability,software quality metrics,McCabe,C&K,Understand

References

1. National Institue of Standards and Technology, ‘The Economic impacts of inadequate infrastructure for software testing’, 2002. [Online]. Available: https://www.nist.gov/sites/default/files/documents/director/planning/report02-3.pdf. [Accessed: 10- Oct- 2018].
2. Tricentis.com, ‘software fail watch 5th edition’, 2017. [Online]. Available: https://www.tricentis.com/wp-content/uploads/2018/01/20180119_Software-Fails-Watch_Small_Web.pdf. [Accessed: 10- Oct- 2018].
3. Jung H., Kim S., and Chung C., “Measuring software product quality: a survey of ISO/IEC 9126”, September/October 2004, IEEE Software, p:88-92.
4. Y. Fitrisia and B. Hendradjaya, "Implementation of ISO 9126-1 quality model for asset inventory information system by utilizing object oriented metrics," 2014 International Conference on Electrical Engineering and Computer Science (ICEECS), Kuta, 2014, pp. 229-234.
5. U. L. Kulkarni, Y. R. Kalshetty and V. G. Arde, "Validation of CK metrics for object oriented design measurement," 2010 3rd International Conference on Emerging Trends in Engineering and Technology, Goa, 2010, pp. 646-651.
6. Antony P., “Predicting reliability of software using thresholds of CK metrics”, 2013 International Journal of Advanced Networking, pp. 1778–1785.
7. Yuming Zhou and Hareton Leung, "Empirical analysis of object-oriented design metrics for predicting high and low severity faults," in IEEE Transactions on Software Engineering, vol. 32, no. 10, pp. 771-789, Oct. 2006.
8. Mago J., Kaur P., “Analysis of quality of the design of the object oriented software using fuzzy logic.” 2012, International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT2012) Proceedings Published in International Journal of Computer Applications® (IJCA), pp. 21–25

9. A.D. Bakar, A. Sultan, H. Zulzalil and J. Din, “Predicting maintainability of object-oriented software using metric threshold.” 2014 Information Technology Journal, 13: 1540-1547.
10. S. Singh, M. Kaur, “Deriving and validating software metrics threshold values for design errors” 2016, International Journal of Engineering Technology and Scientific Research Volume 2 Issue 2.
11. Kıral, A., Ayyıldız, T.E., “Comparison of software quality metrics: a case study.” 2018, 12th Turkish National Software Engineering Symposium (UYMS'18), Istanbul, TURKEY, 10-12 September, in press.
12. K. Yamashite et al., “Thresholds for size and complexity metrics: a case study from the perspective of defect density,” 2016 IEEE International Conference on Software Quality, Reliability and Security (QRS), Vienna, 2016, pp. 191-201.
13. G. Marcela et al. “A survey of metrics for UML class diagrams.” Journal of Object Technology 4 (2005): 59-92.
14. S. Demeyer, S. Ducasse and M. Lanza, "A hybrid reverse engineering approach combining metrics and program visualisation," Sixth Working Conference on Reverse Engineering (Cat. No.PR00303), Atlanta, GA, USA, 1999, pp. 175-186.
15. Mccabe.com, “All metrics thresholds in McCabe iq”, [Online]. Available: http://www.mccabe.com/pdf/McCabe%20IQ%20Metrics.pdf. [Accesed: 7- Oct- 2018].