Abstract
This paper studied process metrics in detail for predicting defects in an open source mobile applications in continuation with our previous study (Moser et al. Software Engineering, 2008). Advanced modeling techniques have been applied on a vast dataset of mobile applications for proving that process metrics are better predictor of defects than code metrics for mobile applications. Mean absolute error, Correlation Coefficient and root mean squared error are determined using different machine learning techniques. In each case it was concluded that process metrics as predictors are significantly better than code metrics as predictors for bug prediction. It is shown that process metrics based defect prediction models are better for mobile applications in all regression based techniques, machine learning techniques and neuro-fuzzy modelling. Therefore separate model has been created based on only process metrics with large dataset of mobile application.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arvinder Kaur, Kamaldeep Kaur, Harguneet Kaur “ A Comparative Study of the Accuracy of Code and Process Metrics for Defect Prediction of Mobile Applications”.
Jureczko, Marian, and Lech Madeyski. “A review of process metrics in defect prediction studies.” Metody Informatyki Stosowanej 5 (2011): 133–145.
Madeyski, Lech, and Marian Jureczko. “Which process metrics can significantly improve defect prediction models? An empirical study.” Software Quality Journal (2014): 1–30.
D’Ambros, Marco, Michele Lanza, and Romain Robbes. “An extensive comparison of bug prediction approaches.” Mining Software Repositories (MSR), 2010 7th IEEE Working Conference on. IEEE, 2010.
Jureczko, Marian, and Diomidis Spinellis. “Using object-oriented design metrics to predict software defects.” Models and Methods of System Dependability. Oficyna Wydawnicza Politechniki Wrocławskiej (2010): 69–81.
O’Keeffe, Mark, and Mel O. Cinnéide. “Search-based software maintenance.” Software Maintenance and Reengineering, 2006. CSMR 2006. Proceedings of the 10th European Conference on. IEEE, 2006: pp. 10.
Yogesh Singh, Arvinder Kaur, and Ruchika Malhotra. “Empirical validation of object-oriented metrics for predicting fault proneness models.” Software quality journal 18.1 (2010): 3–35.
Graves, T. L., Karr, A. F., Marron, J. S., Siy, H. 2000. Predicting fault incidence using software change history. IEEE Transactions on Software Engineering, 26(7): 653–661 (July 2000).
Jureczko, Marian. “Significance of different software metrics in defect prediction.” Software Engineering: An International Journal 1.1 (2011): 86–95.
Malhotra, Ruchika, Nakul Pritam, and Yogesh Singh. “On the applicability of evolutionary computation for software defect prediction.” Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on. IEEE, 2014, 2249–2257.
Moser, Raimund, Witold Pedrycz, and Giancarlo Succi. “A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction.” Software Engineering, 2008, 181–190.
Malhotra, Ruchika, and Rajeev Raje. “An empirical comparison of machine learning techniques for software defect prediction.” Proceedings of the 8th International Conference on Bioinspired Information and Communications Technologies. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2014, 320–327.
Catal, Cagatay, and Banu Diri. “A systematic review of software fault prediction studies.” Expert systems with applications 36.4 (2009): 7346–7354.
Shihab, Emad, et al. “Understanding the impact of code and process metrics on post-release defects: a case study on the eclipse project.” Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement. ACM, 2010, p. 4.
Rahman, Foyzur, and Premkumar Devanbu. “How, and why, process metrics are better.” Proceedings of the 2013 International Conference on Software Engineering. IEEE Press, 2013, pp. 432–441.
Wahyudin, Dindin, et al. “Defect Prediction using Combined Product and Project Metrics-A Case Study from the Open Source” Apache” MyFaces Project Family.” Software Engineering and Advanced Applications, 2008. SEAA’08. 34th Euromicro Conference. IEEE, 2008, pp 207–215.
Johari, Kalpana, and Arvinder Kaur. “Validation of object oriented metrics using open source software system: an empirical study.” ACM SIGSOFT Software Engineering Notes 37.1 (2012): 1–4.
S. Chidamber, and C. Kemerer, “A metrics suite for object oriented design”, IEEE Transactions on Software Engineering, vol. 20, no. 6, pp. 476-493, 1994.
ckjm—Chidamber and Kemerer Java Metrics -http://www.spinellis.gr/sw/ckjm/.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Kaur, A., Kaur, K., Kaur, H. (2016). Application of Machine Learning on Process Metrics for Defect Prediction in Mobile Application. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 433. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2755-7_10
Download citation
DOI: https://doi.org/10.1007/978-81-322-2755-7_10
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2753-3
Online ISBN: 978-81-322-2755-7
eBook Packages: EngineeringEngineering (R0)