Abstract
Changes and their impacts to large-scale enterprise systems are critical and hard to identify and calculate. This work focuses on analysing changes and their potential impacts, and in particular on how regression testing following such changes can be minimised. The target scope of the approach we describe here is systems containing hundreds of thousands of classes and millions of methods. It is extremely difficult and costly to apply regular regression testing techniques to such systems. It is very expensive and often unnecessary to retest everything after a change is introduced. Selective retesting is dangerous if the impacts of change are not understood, and analysing such systems to understand what is being changed and what the impacts are is difficult. This paper proposes a way to perform a change impact analysis which makes it possible to do efficient, targeted regression testing of enterprise systems. Our approach has been tried on a large system comprising 4.6 million methods with 10 million dependencies between them. Using our approach, maintainers can focus on a smaller, relevant subset of their test suites instead of doing testing blindly. We include a case study that illustrates the savings that can be attained.
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References
IT Key Metrics Data 2012. Gartner, Inc., December 2011
Bohner, S.A.: Software change impact analysis. In: Proceedings of the 27th Annual NASA Goddard/IEEE Software Engineering, Workshop (SEW-27’02) (1996)
Ren, X., Shah, F., Tip, F., Ryder, B.G., Chesley, O.: Chianti: a tool for change impact analysis of java programs. SIGPLAN Not. 39, 432–448 (2004)
Pfleeger, S., Atlee, J.: Software Engineering: Theory and Practice. Prentice Hall, Englewood Cliffs (2006)
Ayewah, N., Hovemeyer, D., Morgenthaler, J., Penix, J., Pugh, W.: Using static analysis to find bugs. IEEE Softw. 25, 22–29 (2008)
Khare, S., Saraswat, S., Kumar, S.: Static program analysis of large embedded code base: an experience. In: Proceedings of the 4th India Software Engineering Conference, ISEC ’11, (New York, NY, USA), pp. 99–102. ACM (2011)
Apiwattanapong, T.: Efficient and precise dynamic impact analysis using execute-after sequences. In: Proceedings of the 27th International Conference on Software Engineering (2005)
Orso, A., Apiwattanapong, T., Harrold, M.J.: Leveraging field data for impact analysis and regression testing. In: Proceedings of the 9th European Software Engineering Conference held jointly with 11th ACM SIGSOFT International Symposium on Foundations of Software Engineering, vol. 28(5), September 2003
Breech, B., Danalis, A., Shindo, S., Pollock, L.: Online impact analysis via dynamic compilation technology. In: 20th IEEE International Conference on Software Maintenance (2004)
Patel, C., Hamou-Lhadj, A., Rilling, J.: Software clustering using dynamic analysis and static dependencies. In: 13th European Conference on Software Maintenance and Reengineering, 2009. CSMR ’09, pp. 27–36. March 2009
Li, H.: Dynamic analysis of object-oriented software complexity. In: 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), pp. 1791–1794, April 2012
Law J., Rothermel, G.: Incremental dynamic impact analysis for evolving software systems., In: Proceedings of the 14th International Symposium on Software, Reliability Engineering (2003)
Maia, M.C.O., Bittencourt, R.A., de Figueiredo, J.C.A., Guerrero, D.D.S.: The hybrid technique for object-oriented software change impact analysis. In: European Conference on Software Maintenance and Reengineering, pp. 252–255 (2010)
Ferrante, J., Ottenstein, K.J., Warren, J.D.: The program dependence graph and its use in optimization. ACM Trans. Program. Lang. Syst. 9, 319–349 (1987)
Ottenstein, K.J., Ottenstein, L.M.: The program dependence graph in a software development environment. SIGPLAN Not. 19, 177–184 (1984)
Rothermel, G., Harrold, M.: Analyzing regression test selection techniques. IEEE Trans. Softw. Eng. 22, 529–551 (1996)
Apiwattanapong, T., Orso, A., Harrold, M.: A differencing algorithm for object-oriented programs. In: Proceedings of the 19th International Conference on Automated Software Engineering 2004, pp. 2–13. September 2004
Canfora, G., Cerulo, L.: Impact analysis by mining Software and Change Request Repositories. In: IEEE International Symposium on Software Metrics, pp. 20–29 (2005)
Orso, A., Shi, N., Harrold, M.J.: Scaling regression testing to large software systems. SIGSOFT Softw. Eng. Notes 29, 241–251 (2004)
Bacon, D., Sweeney, P.: Fast static analysis of C++ virtual function calls. In: Proceedings of the Conference on Object-Oriented Programming Systems, Languages, and Applications, ACM SIGPLAN Notices, vol. 31, pp. 324–341. ACM Press, New York, October 1996
Lam, P., Bodden, E., Lhotak, O., Lhotak, J., Qian, F., Hendren, L.: Soot: A Java Optimization Framework. Sable Research Group, McGill University, Montreal, Canada, March 2010. http://www.sable.mcgill.ca/soot/
Chen, W., Iqbal, A., Abdrakhmanov, A., George, C., Lawford, M., Maibaum, T., Wassyng, A.: Report 7: Middleware Change Impact Analysis for Large-scale Enterprise Systems. Tech. Rep. 7, McMaster Centre for Software Certification (McSCert), September 2011
Christensen, A.S., Møller, A., Schwartzbach, M.I.: Precise analysis of string expressions. In: Cousot, R. (ed.) SAS 2003. LNCS, vol. 2694, pp. 1–18. Springer, Heidelberg (2003). http://www.brics.dk/JSA/
Tarjan, R.: Depth-first search and linear graph algorithms. SIAM J. Comput. 1(2), 146–160 (1972)
Mockus, A., Weiss, D.M.: Predicting risk of software changes. Bell Labs Tech. J. 5(2), 169–180 (2000)
Doar, M.B.: JDiff - An HTML Report of API Differences (2007). http://javadiff.sourceforge.net/
Jar Compare Tool. http://www.extradata.com/products/jarc/
Tessier, J.: The Dependency Finder User Manual, November 2010. http://depfind.sourceforge.net/Manual.html
Acknowledgments
The authors are grateful to Ron Mison, Sundaram Viswanathan, and Vinayak Viswanathan of Legacy Systems International, for introducing us to the problem, and also for working with us to ensure that our research produces practical methods and tools. We also thank John Hatcliff for his advice and for pointing us to relevant work, and Wolfram Kahl for his technical advice throughout the project.
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Chen, W. et al. (2013). Large-Scale Enterprise Systems: Changes and Impacts. In: Cordeiro, J., Maciaszek, L.A., Filipe, J. (eds) Enterprise Information Systems. Lecture Notes in Business Information Processing, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40654-6_17
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