Abstract
Software engineering includes some different process such as designing, implementing and modifying of software. All of these processes are done to have fast developed software as well as reach a high quality, efficient and maintainable software. Invariants help programmer and tester to do most steps of software engineering more easily. Invariants are mostly always true but of course with a specific confidence. Since some invariants are produced on some conditions of program execution and not always, conditional invariants can show the behavior of program so much better. For producing this kind of invariants, it might be used some technique of data mining such as association rule mining or using decision tree to obtain rules. So the paper will introduce a new perspective to dynamic invariant detection. Also the feasibility of conditional invariant detection is examined and a framework to extract them is proposed.
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References
Krkay, I., Brunx, Y., Popescuy, D., Garciay, J., Medvidovic, N.: Using dynamic execution traces and program invariants to enhance behavioral model inference. In: ICSE NIER (2010)
Vanmali, M., Last, M., Kandel, A.: Using a neural network in the software testing process. International Journal of Intelligent Systems 17(1), 45–62 (2002)
Ernst, M.D., Cockrell, J., Griswold, W.G., Notkin, D.: Dynamically discovering likely program invariants to support program evolution. IEEE TSE 27(2), 99–123 (2007)
Ernst, M.D., et al.: Dynamically discovering likely program invariants to support program evolution. In: Proc. ICSE 1999, pp. 213–224. ACM, New York (1999)
Weiß, B.: Inferring invariants by static analysis in KeY. Diplomarbeit, University of Karlsruhe (March 2007)
Jones, N.D., Nielson, F.: Jones and Flemming Nielson. Abstract interpretation: A semanticsbased tool for program analysis. In: Abramsky, S., Gabbay, D.M., Maibaum, T.S.E. (eds.) Handbook of Logic in Computer Science, vol. 4, pp. 527–636. Oxford University Press, Oxford (1995)
Ernst, M.D., Perkins, J.H., Guo, P.J., McCamant, S., Pacheco, C., Tschantz, M.S., Xiao, C.: The Daikon System for Dynamic Detection of Likely Invariants. Science of Computer Programming (2006)
Csallner, C., et al.: DySy: Dynamic symbolic execution for invariant inference. In: Proc. of ICSE (2008)
Boshernitsan, M., Doong, R., Savoia, A.: From Daikon to Agitator: Lessons and challenges in building a commercial tool for developer testing. In: ISSTA, pp. 169–179 (2006)
Hangal, S., Lam, M.S.: Tracking down software bugs using automatic anomaly detection. In: ICSE, pp. 291–301 (2002)
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Fouladgar, H., Minaei-Bidgoli, B., Parvin, H. (2011). On Possibility of Conditional Invariant Detection. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowlege-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6882. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23863-5_22
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DOI: https://doi.org/10.1007/978-3-642-23863-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23862-8
Online ISBN: 978-3-642-23863-5
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