Abstract
In the field of software engineering, during the development ofObject Oriented (OO) software, the knowledge of the classes which are more prone tochanges in software is an important problem that arises nowadays. In order to solve this problem, several methods were introduced by predicting the changes in the software earlier. But those methods are not facilitating very good prediction result. This research work proposes a novel approach for predicting changes in software. Our proposed probabilistic approach uses the behavioral dependency generated from UML diagrams, as well as other code metrics such as time and trace events generated from source code. These measures combined with frequency of method calls and popularity can be used in automated manner to predict a change prone class. Thus all these five features (time, trace events, behavioral dependency, frequency and popularity) are obtained from our proposed work. Then, these features are given as the input to the ID3 (Interactive Dichotomizer version 3) decision tree algorithm for effectively classifying the classes, whether it predicts the change proneness or not. If a class is classified into prediction of change prone class, then the value of change proneness is also obtained by our work.
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
Preview
Unable to display preview. Download preview PDF.
References
Pataricza, A., Majzik, I., Huszerl, G., Várnai, G.: UML-based Design and Formal Analysis of a Safety-Critical Railway Control Software Module. In: Proceedings of the Conference on Formal Method for Railway Operations and Control Systems (2003)
Güneş Koru, A., Liu, H.: Identifying and characterizing change-prone classes in two large-scale open-source products. Journal of Systems and Software 80(1), 63–73 (2007)
Omerovic, A., Andresen, A., Grindheim, H., Myrseth, P., Refsdal, A., Stølen, K., Ølnes, J.: Idea: a feasibility study in model based prediction of impact of changes on system quality. In: Massacci, F., Wallach, D., Zannone, N. (eds.) ESSoS 2010. LNCS, vol. 5965, pp. 231–240. Springer, Heidelberg (2010)
Glasberg, D., Emam, K.E., Melo, W., Madhavji, N.: Validating Object-Oriented Design Metrics on a Commercial Java Application. National Research Council (September 2000)
Romano, D., Pinzger, M.: Using Source Code Metrics to Predict Change-Prone Java Interfaces. In: Proceedings of 27th IEEE International Conference on Software Maintenance, pp. 303–312 (2011)
Arisholm, E., Sjøberg, D.I.K.: Towards a framework for empirical assessment of changeability decay. The Journal of Systems and Software 53(1), 3–14 (2000)
Arisholm, E., Briand, L.C., Føyen, A.: Dynamic Coupling Measurement for Object-Oriented Software. IEEE Transactions on Software Engineering 30(8), 491–506 (2004)
Bieman, J.M., Andrews, A.A., Yang, H.J.: Understanding Change-proneness in OO Software through Visualization. In: Proceedings of the International Workshop on Program Comprehension (2003)
Thramboulidis, K.C.: Using UML for the Development of Distributed Industrial Process Measurement and Control Systems. In: Proceedings of IEEE Conference on Control Applications, pp. 1129–1134 (September 2001)
Nguyen, K.D., Thiagarajan, P.S., Wong, W.-F.: A UML-Based Design Framework for Time-Triggered Applications. In: Proceedings of 28th IEEE International Symposium on Real-Time Systems, pp. 39–48 (2007)
Lindvall, M.: Measurement of Change: Stable and Change-Prone Constructs in a Commercial C++ System. In: Proceedings of IEEE 6th International Software Metrics Symposium, pp. 40–49 (1999)
Kušek, M., Desic, S., Gvozdanović, D.: UML Based Object-oriented Development: Experience with Inexperienced Developers. In: Proceedings of 6th International Conference on Telecommunications, pp. 55–60 (June 2001)
Abdi, M.K., Lounis, H., Sahraoui, H.: A probabilistic Approach for Change Impact Prediction in Object-Oriented Systems. In: Proceedings of 2nd Artificial Intelligence Methods in Software Engineering Workshop (2009)
Tsantalis, N., Chatzigeorgiou, A., Stephanides, G.: Predicting the Probability of Change in Object-Oriented Systems. IEEE Transactions on Software Engineering 31(7), 601–614 (2005)
Garousi, V., Briand, L.C., Labiche, Y.: Analysis and visualization of behavioral dependencies among distributed objects based on UML models. In: Wang, J., Whittle, J., Harel, D., Reggio, G. (eds.) MoDELS 2006. LNCS, vol. 4199, pp. 365–379. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Godara, D., Singh, R.K. (2015). Enhancing Frequency Based Change Proneness Prediction Method Using Artificial Bee Colony Algorithm. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-11218-3_48
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11217-6
Online ISBN: 978-3-319-11218-3
eBook Packages: EngineeringEngineering (R0)