Abstract
Dynamic detection of program invariants infers relationship between variables at program points using trace data, but reports a large number of irrelevant invariants. We outline an approach that combines lightweight static analysis with dynamic inference that restricts irrelevant comparisons. This is achieved by constructing a variable dependence graph relating a procedure’s input and output variables. Initial experiments indicate the advantage of this approach over the dynamic analysis tool Daikon.
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Yeolekar, A. (2014). Improving Dynamic Inference with Variable Dependence Graph. In: Bonakdarpour, B., Smolka, S.A. (eds) Runtime Verification. RV 2014. Lecture Notes in Computer Science, vol 8734. Springer, Cham. https://doi.org/10.1007/978-3-319-11164-3_25
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DOI: https://doi.org/10.1007/978-3-319-11164-3_25
Publisher Name: Springer, Cham
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