Abstract
Program logical error localization and program testing are two of the most important sections in software engineering. Programmers or companies that produce programs will lose their credit and profit effectively if one of their programs delivered to a customer has any drawback. Nowadays there are many methods to test a program. This paper suggests a framework to localize the program logical errors by extraction of knowledge from invariants using a clustering technique.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Catal, C., Diri, B.: Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem. Information Sciences 179(8), 1040–1058 (2009)
Catal, C.: Software fault prediction: A literature review and current trends. Expert Systems with Applications 38, 4626–4636 (2011)
Cook, J.E., Wolf, A.L.: Discovering Models of Software Processes from Event-Based Data. ACM Trans. Software Eng. and Methodology (1998a)
Cook, J.E., Wolf, A.L.: Event-Based Detection of Concurrency. In: Proc. ACM SIGSOFT Symp. (1998b)
Dwyer, M.B., Clarke, L.A.: Data Flow Analysis for Verifying Properties of Concurrent Programs. In: Proc. Second ACM SIGSOFT Symp. (1994)
Ernst, M.D., Griswold, W.G., Kataoka, Y., Notkin, D.: Dynamically Discovering Program Invariants Involving Collections. Technical Report UW-CSE-99-11-02 (2000)
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)
Evans, D., Guttag, J., Horing, J., Tan, Y.M.: LCLint:A Tool for Using Specification to Check Code. In: Proc. Second ACM SIGSOFT Symp. (1994)
Kataoka, Y., Ernst, M.D., Griswold, W.G., Notkin, D.: Automated Support for Program Refactoring Using Invariants. In: Proc. Int’l Conf. Software Maintenance (2001)
Lencevicius, R., Ho Èlzle, U., Singh, A.K.: Query-Based Debugging of Object-Oriented Programs. In: Proc. Conf. Object-Oriented Programming, Systems, Languages, and Applications (1997)
Menzies, T., Greenwald, J., Frank, A.: Data mining static code attributes to learn defect predictors. IEEE Transactions on Software Engineering 33(1), 2–13 (2007)
Mitchell, M.: An Introduction to Genetic Algorithms. The MIT Press, Cambridge (1998)
Moraglio, A., Kim, Y.H., Yoon, Y., Moon, B.R., Poli, R.: Generalized cycle crossover for graph partitioning. In: GECCO (2006)
Nimmer, J.W., Ernst, M.D.: Automatic Generation of Program Specifications. In: Proc. Int’l Symp. Software Testing and Analysis (2002)
Perkins, J.H., Ernst, M.D.: Efficient Incremental Algorithms for Dynamic Detection of Likely Invariants. In: Proc. ACM SIGSOFT Symp. (2004)
Schmitt, H., Weiß, B.: Inferring Invariants by Static Analysis in KeY (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Daryabari, M., Minaei-Bidgoli, B., Parvin, H. (2011). Localizing Program Logical Errors Using Extraction of Knowledge from Invariants. In: Pardalos, P.M., Rebennack, S. (eds) Experimental Algorithms. SEA 2011. Lecture Notes in Computer Science, vol 6630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20662-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-20662-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20661-0
Online ISBN: 978-3-642-20662-7
eBook Packages: Computer ScienceComputer Science (R0)