Abstract
Invariants could be defined as prominent relation among program variables. Daikon software has implemented a practical algorithm for invariant detection. There are several other dynamic approaches to dynamic invariant detection. Daikon is considered to be the best software developed for dynamic invariant detection in comparing other dynamic invariant detection methods. However this method has some problems. Its time order is highly which this results in uselessness in practice. The bottleneck of the algorithm is predicate checking. In this paper, two new techniques are presented to improve the performance of the Daikon algorithm. Experimental results show that With regard to these amendments, runtime of dynamic invariant detection is much better than the original method.
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Parsa, S., Minaei, B., Daryabari, M., Parvin, H. (2011). New Efficient Techniques for Dynamic Detection of Likely Invariants. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_39
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DOI: https://doi.org/10.1007/978-3-642-20282-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20281-0
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