Risk chain prediction metrics for predicting fault proneness in object oriented systems
Pages 490 - 496
Abstract
The paper presents two atypical risk chain prediction metrics for barometer coupling and accord in software systems. Our aboriginal metric, Ideal Coupling between Object classes (ICBO), is based on the acclaimed CBO coupling metric, while the added metric, Ideal Lack of Cohesion on Methods (ILCOM5), is based on the LCOM5 accord metric. One advantage of the proposed risk chain prediction metrics is that they can be computed in a simpler way as compared to some of the structural metrics. We empirically advised ICBO and ILCOM5 for admiration fault proneness of classes in a ample accessible antecedent arrangement and compared these metrics with a host of absolute structural and risk chain prediction metrics for the aforementioned task.
References
[1]
Antoniol, G., Canfora, G., Casazza, G., De Lucia, A., and Merlo, E.,"Recovering Traceability Links between Code and Documentation", IEEE Transactions on Soft. Engineering, vol. 28, no. 10, pp. 970--983. 2002
[2]
Antoniol, G., Fiutem, R., and Cristoforetti, L., "Using Metrics to Identify Design Patterns in Object-Oriented Software", in Proc. of 5th IEEEMETRICS'98, Bethesda, MD, pp. 23--34., 1998
[3]
Arisholm, E., Briand, L. C., and Foyen, A., "Dynamic coupling measurement for OO software", IEEE TSE, vol. 30, no. 8, pp. 491--506., 2004
[4]
Bansiya, J. and Davis, C. G., "A hierarchical model for object-oriented design quality assessment", IEEE TSE, vol. 28, no. 1, pp. 4--17., 2002
[5]
Basili, V. R., Briand, L. C., and Melo, W. L., "A Validation of OO Design Metrics as Quality Indicators", IEEE TSE, vol. 22/10, Oct., pp. 751--761., 1996
[6]
Basili, V. R., Caldiera, G., and Rombach. D. H., The Goal Question Metric Paradigm, John W & S, 1994.
[7]
Briand, L., Melo, W., and Wust, J., "Assessing the Applicability of Fault-Proneness Models across OO Software Projects", TSE, vol.28/7, 706--720., 2002
[8]
Briand, L., Wust, J., and Louinis, H., "Using Coupling Measurement for Impact Analysis in OO Systems", in IEEE ICSM'99, pp. 475--482., 19996
[9]
Briand, L. C., Daly, J., and Wüst, J., "A Unified Framework for Coupling Measurement in OO Systems", IEEE TSE, vol. 25/1, pp. 91--121., 1999
[10]
Briand, L. C., Wüst, J., Daly, J. W., and Porter, V. D., "Exploring the relationship between design measures and software quality in object-oriented systems", Journal of System and Software, vol. 51, no. 3, pp. 245--273., 2000
[11]
Caprile, B. and Tonella, P., "Restructuring Program Identifier Names", inProc. of 16th IEEE ICSM'00, San Jose, California, USA, pp. 97--107., 2000
[12]
Chidamber, S. R. and Kemerer, C. F., "Towards a Metrics Suite for Object-oriented Design", in Proc. of OOPSLA'91, pp. 197--211., 1991
[13]
De Lucia, A., Fasano, F., Oliveto, R., and Tortora, G., "Recovering Traceability Links in Software Artifact Management Systems", TOSEM, vol.16, no. 4, 2007.
[14]
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R., "Indexing by Latent Semantic Analysis", Journal of the American Society for Information Science, vol. 41, pp. 391--407., 1990
[15]
Deissenboeck, F. and Pizka, M., "Concise and Consistent Naming", Software Quality Journal, vol. 14, no. 3, pp. 261--282, 2006
[16]
Dit, B., Poshyvanyk, D., and Marcus, A., "Measuring the Semantic Similarity of Comments in Bug Reports", in Proc. of 1st STSM'08, 2008
[17]
El-Emam, K. and Melo, K., "The Prediction of Faulty Classes Using Object-Oriented Design Metrics", NRC/ERB-1064, vol. NRC 43609, 1999.
[18]
Enslen, E., Hill, E., Pollock, L., and Vijay-Shanker, K., "Mining Source Code to Automatically Split Identifiers for Software Analysis", in Proc. of 6thIEEE MSR, Vancouver, BC, Canada, pp. 71--80., 2009
[19]
Ferenc, R., Beszedes, A., and Gyimóthy, T., "Extracting Facts with Columbus from C++ Code", in Proc. of 8th CSMR"04, March, pp. 4--8., 2004
[20]
Ferenc, R., Beszédes, Á., Tarkiainen, M., and Gyimóthy, T., "Columbus -Reverse Engineering Tool and Schema for C++", in ICSM'02, 172--181, 2002
[21]
Fluri, B., Würsch, M., Giger, E., and Gall, H., "Analyzing the co-evolution of comments and source code", SQJ, vol. 17/4, pp. 367--394., 2009
[22]
Gyimóthy, T., Ferenc, R., and Siket, I., "Empirical validation of OOmetrics on open source software for fault prediction", TSE, vol.31/10, Oct' 2005.
[23]
Henderson-Sellers, B., Software Metrics, U. K., Prentice Hall, 1996.
[24]
Hill, E., Fry, Z. P., Boyd, H., Sridhara, G., Novikova, Y., Pollock, L., and Vijay-Shanker, K., "AMAP: Automatically Mining Abbreviation Expansionsin Programs to Enhance Software Maintenance Tools", in MSR'08.
[25]
Kuhn, A., Ducasse, S., and Gîrba, T., "Semantic Clustering: Identifying Topics in Source Code", IST, vol. 49/3, pp. 230--243, 2007
[26]
Lawrie, D., Feild, H., and Binkley, D., "Leveraged Quality Assessment Using Information Retrieval Techniques", in ICPC'06, pp. 149--158., 2006
[27]
Lawrie, D., Feild, H., and Binkley, D., "Extracting Meaning from Abbreviated Identifiers", in Proc. of 7th IEEE SCAM'07, Paris, France, 2007.
[28]
Liu, Y., Poshyvanyk, D., Ferenc, R., Gyimóthy, T., and Chrisochoides, N.,"Modeling Class Cohesion as Mixtures of Latent Topics", in ICSM'09.
[29]
Maletic, J. I. and Marcus, A., "Supporting Program Comprehension Using Semantic and Structural Information", in ICSE'01, pp. 103--112., 2001
[30]
Marcus, A. and Maletic, J. I., "Recovering Documentation-to-Source-Code Traceability Links using LSI", in ICSE'03, pp. 125--137.
[31]
Marcus, A. and Poshyvanyk, D., "The Ideal Cohesion of Classes", in Proc. of 21st IEEE ICSM'05, Budapest, Hungary, pp. 133--142., 2005
[32]
Marcus, A., Poshyvanyk, D., and Ferenc, R., "Using the Ideal Cohesion of Classes for Fault Prediction in OO Systems", TSE, vol. 34/2, pp.287--300., 2008
[33]
Olague, H., Etzkorn, L., Gholston, S., and 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 TSE, vol. 33, no. 6, pp. 402--419., 2007
[34]
Poshyvanyk, D., Guéhéneuc, Y. G., Marcus, A., Antoniol, G., and Rajlich, V., "Feature Location using Probabilistic Ranking of Methods based on Execution Scenarios and IR", TSE, vol. 33/6, pp. 420--432., 2007
[35]
Poshyvanyk, D. and Marcus, A., "The Ideal Coupling Metrics for Object-Oriented Systems", in IEEE ICSM'06, Phil., PA, pp. 469--478., 2006
[36]
Poshyvanyk, D., Marcus, A., Ferenc, R., and Gyimóthy, T., "Using Information Retrieval based Coupling Measures for Impact Analysis", Empirical Software Engineering, vol. 14, no. 1, pp. 5--32., 2009
[37]
Quah, T.-S. and Thwin, M. M. T., "Application of neural networks for software quality prediction using OO metrics", in ICSM'03, pp. 116--125.
[38]
Salton, G. & McGill, M., Introduction to Modern IR, McGraw-Hill, 1983.
[39]
Subramanyam, R. and Krishnan, M. S., "Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects", IEEE TSE, vol. 29, no. 4, pp. 297--310., 2003
[40]
Tairas, R. and Gray, J., "An Information Retrieval Process to Aid in the Analysis of Code Clones", ESE, vol. 14, no. 1, pp. 33--56., 2009
[41]
Wilkie, F. G. and Kitchenham, B. A., "Coupling measures and change ripples in C++ application software", JSS, vol. 52, pp. 157--164., 2000
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Published: 26 October 2012
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