Abstract
The notion of predicting fault-proneness by utilizing software metric data has acquired the attention of many researchers in the past three decades. The fault-proneness prediction can assist in the systematic distribution of the software development resources, as testers need to put efforts and time on only those software classes where the chances of faults are very high. This study investigates the dichotomization capability of thresholds identified through Receiver Operating Characteristic (ROC) curve and F-measure. These methods were utilized to compute the cut-off values of the software measures extracted from the jEdit software system. Besides Chidamber and Kemerer metric suite, we also assessed the prediction capability of a dynamic measure - Dynamic Coupling between Object classes (DCBO). The dynamic metrics are capable of revealing true execution behaviour of the software system as here the values can only be extracted at the run-time, therefore can handle the object oriented features, such as, polymorphism, inheritance, and dynamic binding, better than the static metrics. The experimental results highlighted the good performance of the DCBO measure, indicating this particular metric as a promising candidate for the purpose of fault proneness prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Shatnawi, R., Li, W., Swain, J., Tim, N.: Finding software metrics threshold values using ROC. J. Softw. Maint. Evol. Pract. 22, 1–16 (2010)
Mccabe, J.: A Complexity Measure, vol. SE-2, no. 4, pp. 308–320 (1994).
Nejmeh, B.A.: Npath: a measure of execution path complexity and its applications. Commun. ACM 31(2), 188–200 (1988)
Erni, K., Lewerentz, C.: Applying design-metrics to object-oriented frameworks. In: Proceedings of the 3rd International Software Metrics Symposium, pp. 64–74 (1996)
Shatnawi, R.: A quantitative investigation of the acceptable risk levels of object-oriented metrics in open-source systems. IEEE Trans. Softw. Eng. 36(2), 216–225 (2010)
Malhotra, R., Bansal, A.J.: Fault prediction considering threshold effects of object-oriented metrics. Exp. Syst. 32(2), 203–219 (2015)
Singh, S., Kahlon, K.S.: Object oriented software metrics threshold values at quantitative acceptable risk level. CSI Trans. ICT 2(3), 191–205 (2014). https://doi.org/10.1007/s40012-014-0057-1
Arar, K., Ayan, O.F.: Deriving thresholds of software metrics to predict faults on open source software. Exp. Syst. Appl. 61, 106–121 (2016)
Hussain, S., Keung, J., Khan, A.A., Bennin, K.E.: Detection of fault-prone classes using logistic regression based object-oriented metrics thresholds. In: Proceedings - 2016 IEEE International Conference on Software Quality, Reliability and Security QRS-C 2016, pp. 93–100 (2016)
Catal, C., Alan, O., Balkan, K.: Class noise detection based on software metrics and ROC curves. Inf. Sci. (NY) 181(21), 4867–4877 (2011)
Shatnawi, R.: Deriving metrics thresholds using log transformation: deriving metrics thresholds. J. Softw. Evol. Process 27(2), 95–113 (2015). https://doi.org/10.1002/smr.1702
Boucher, A., Badri, M.: Software metrics thresholds calculation techniques to predict fault-proneness: an empirical comparison. Inf. Softw. Technol. 96, 38–67 (2017)
Mitchell, J.F., Power, A.: Run-time coupling metrics for the analysis of Java programs-preliminary results from the SPEC and grande suites (2003)
Mitchell, J.F., Power, A.: An empirical investigation into the dimensions of run-time coupling in Java programs, pp. 9–14 (2004)
Hassoun, Y., Johnson, R., Counsell, S.: A dynamic runtime coupling metric for meta-level architectures. In: Proceedings of the European Conference on Software Maintenance and Reengineering, CSMR, pp. 339–346 (2004)
Zaidman, A., Demeyer, S.: Analyzing large event traces with the help of coupling metrics. In: Proceedings of the 5th International Workshop on Object-Oriented Reengineering, Technical Report of the Department of Mathematics and Computer Science of the University of Antwerp (2004)
Arisholm, E., Briand, L.C., Foyen, A.: Dynamic coupling measurement for object-oriented software. IEEE Trans. Softw. Eng. 30(8), 491–506 (2004)
Lavazza, L., Morasca, S., Taibi, D., Tosi, D., Mazzini, V.: On the definition of dynamic software measures, pp. 39–48 (2012)
Chidamber, S.R., Kemerer, C.F.: A metrics suite for object oriented design. IEEE Trans. Softw. Eng. 20(6), 476–493 (1994)
Gupta, V., Chhabra, J.K.: Dynamic cohesion measures for object-oriented software. J. Syst. Archit. 57(4), 452–462 (2011)
Yacoub, S.M., Ammar, H.H., Robinson, T.: Dynamic metrics for object oriented designs. In: Proceedings of the 6th International Software Metrics Symposium, pp. 50–61 (1999)
Geetika, R., Singh, P.: Empirical investigation into static and dynamic coupling metrics. ACM SIGSOFT Softw. Eng. Notes 39(1), 1–8 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kaur, N., Singh, H. (2021). On the Comparison of Static and Dynamic Metrics Toward Fault-Proneness Prediction. In: Luhach, A.K., Jat, D.S., Bin Ghazali, K.H., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2020. Communications in Computer and Information Science, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-3660-8_37
Download citation
DOI: https://doi.org/10.1007/978-981-16-3660-8_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3659-2
Online ISBN: 978-981-16-3660-8
eBook Packages: Computer ScienceComputer Science (R0)