Abstract
The use of model-based diagnosis for automated program debugging has been reported in several publications. The quality of the obtained results in terms of debugging accuracy is good. Unfortunately, most of the proposed models and techniques have very high computational needs. In this paper we focus on giving an explanation for the high computational needs of debugging. In particular, we propose a constraint representation of programs whose behavior is equivalent to the original programs. We further analyze the constraint representation to obtain its hypertree width, which is an indicator for the complexity of the corresponding constraint satisfaction problem. As constraint-based debugging is equivalent to constraint solving, the hypertree width is also an indicator for the debugging complexity. We further show that it is possible to construct arbitrarily complex programs such that their hypertree width is not bounded as indicated in previous literature.
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References
Brandis, M.M., Mössenböck, H.: Single-pass generation of static assignment form for structured languages. ACM TOPLAS 16(6), 1684–1698 (1994)
Dechter, R.: Constraint Processing. Morgan Kaufmann, San Francisco (2003)
Gottlob, G., Leone, N., Scarcello, F.: A comparison of structural CSP decomposition methods. Artificial Intelligence 124(2), 243–282 (2000)
DeMillo, R.A., Pan, H., Spafford, E.H.: Critical slicing for software fault localization. In: International Symposium on Software Testing and Analysis (ISSTA), pp. 121–134 (1996)
Mayer, W., Stumptner, M., Wieland, D., Wotawa, F.: Can ai help to improve debugging substantially? debugging experiences with value-based models. In: Proceedings of the European Conference on Artificial Intelligence, Lyon, France, pp. 417–421 (2002)
Thorup, M.: All Structured Programs have Small tree width and Good Register Allocation. Information and Computation Journal
Dermaku, A., Ganzow, T., Gottlob, G., McMahan, B., Musliu, N., Samer, M.: Heuristic Methods for hypertree Decompositions, DBAI-TR-2005-53, Technische Universität Wien (2005)
Mayer, W.: Static and Hybrid Analysis in Model-based Debugging. PhD Thesis, School of Computer and Information Science, University of South Australia, Adelaide, Australia (2007)
Wotawa, F., Nica, M.: On the Compilation of Programs into their equivalent Constraint Representation. Informatica 32(4), 359–371 (2008)
Ceballos, R., Gasca, R.M., Del Valle, C., Borrego, D.: Diagnosing Errors in DbC Programs Using Constraint Programming. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds.) CAEPIA 2005. LNCS (LNAI), vol. 4177, pp. 200–210. Springer, Heidelberg (2006)
Wotawa, F., Peischl, B.: Automated source level error localization in hardware designs. IEEE Design and Test of Computers 23(1), 8–19 (2006)
Collavizza, H., Rueher, M.: Exploring Different Constraint-Based Modelings for Program Verification. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 49–63. Springer, Heidelberg (2007)
Reiter, R.: A theory of diagnosis from first principles. Artificial Intelligence 32(1), 57–95 (1987)
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Wotawa, F., Weber, J., Nica, M., Ceballos, R. (2010). On the Complexity of Program Debugging Using Constraints for Modeling the Program’s Syntax and Semantics. In: Meseguer, P., Mandow, L., Gasca, R.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2009. Lecture Notes in Computer Science(), vol 5988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14264-2_3
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DOI: https://doi.org/10.1007/978-3-642-14264-2_3
Publisher Name: Springer, Berlin, Heidelberg
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