Abstract
Given a program analysis problem that consists of a program and a property of interest, we use an empirical approach to automatically construct a sequence of abstractions that approach an ideal abstraction suitable for solving that problem. This process begins with an infinite concrete domain that maps to a finite abstract cluster domain defined by statistical procedures. Given a set of properties expressed as formulas in a restricted and bounded variant of CTL, we can test the success of the abstraction with respect to a predefined performance measure. In addition, we can perform iterative abstraction-refinement of the clustering by tuning hyperparameters that determine the accuracy of the cluster representations (abstract states) and determine the number of clusters.
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Notes
- 1.
The set of extended reals (\(\mathbb {R}^\ell \cup \{\top , \bot \}\) where for all \(x \in \mathbb {R}^\ell \), \(\bot< x <\top \)) with the usual ordering is a complete lattice. This case also works for our framework.
- 2.
Let \((X, \le )\) be a poset and \(A \subseteq X\). Then A is a downset of X if \(x \in X\), \(x \le y\), \(y \in A\) implies \(x \in A\).
- 3.
A meaningful code location refers to a statement that has a side-effect.
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Ho, V.M., Alvin, C., Mukhopadhyay, S., Peterson, B., Lawson, J.D. (2020). Empirical Abstraction. In: Deshmukh, J., Ničković, D. (eds) Runtime Verification. RV 2020. Lecture Notes in Computer Science(), vol 12399. Springer, Cham. https://doi.org/10.1007/978-3-030-60508-7_14
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