Abstract
Algorithmic debugging is a debugging technique that uses a data structure representing all computations performed during the execution of a program. This data structure is the so-called Execution Tree and it strongly influences the performance of the technique. In this work we present a transformation that automatically improves the structure of the execution trees by collapsing and projecting some strategic nodes. This improvement in the structure implies a better behavior and performance of the standard algorithms that traverse it. We prove that the transformation is sound in the sense that all the bugs found after the transformation are real bugs; and if at least one bug is detectable before the transformation, then at least one bug will also be detectable after the transformation. We have implemented the technique and performed several experiments with real applications. The experimental results confirm the usefulness of the technique.
This work has been partially supported by the Spanish Ministerio de Ciencia e Innovación under grants TIN2008-06622-C03-02 and TIN2012-39391-C04-04, by the Generalitat Valenciana under grant ACOMP/2009/017, and by the Comunidad de Madrid under grant S2009/TIC–1465. David Insa has been partially supported by the Spanish Ministerio de Educación under grant AP2010-4415.
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Insa, D., Silva, J., Riesco, A. (2013). Speeding Up Algorithmic Debugging Using Balanced Execution Trees. In: Veanes, M., Viganò, L. (eds) Tests and Proofs. TAP 2013. Lecture Notes in Computer Science, vol 7942. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38916-0_8
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