Techniques to facilitate probabilistic software analysis in real-world programs
Probabilistic software analysis aims at quantifying how likely a target event is to occur, given a probabilistic characterization of the behavior of a program or of its execution environment. Examples of target events may include an uncaught exception, the invocation of a certain method, or the a...
| Main Author: | BORGES, Mateus Araújo |
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| Other Authors: | D'AMORIM, Marcelo Bezerra |
| Format: | masterThesis |
| Language: | por |
| Published: |
Universidade Federal de Pernambuco
2016
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| Subjects: | |
| Online Access: |
https://repositorio.ufpe.br/handle/123456789/14932 |
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| Summary: |
Probabilistic software analysis aims at quantifying how likely a target event is to occur,
given a probabilistic characterization of the behavior of a program or of its execution environment.
Examples of target events may include an uncaught exception, the invocation of a certain method,
or the access to confidential information. The technique collects constraints on the inputs that
lead to the target events and analyzes them to quantify how likely it is for an input to satisfy the
constraints. Current techniques either handle only linear constraints or only support continuous
distributions using a “discretization” of the input domain, leading to imprecise and costly results.
This work proposes an iterative distribution-aware sampling approach to support probabilistic
symbolic execution for arbitrarily complex mathematical constraints and continuous
input distributions. We follow a compositional approach, where the symbolic constraints are
decomposed into sub-problems whose solution can be solved independently. At each iteration
the convergence rate of the computation is increased by automatically refocusing the analysis
on estimating the sub-problems that mostly affect the accuracy of the results, as guided by
three different ranking strategies. Experiments on publicly available benchmarks show that the
proposed technique improves on previous approaches in terms of scalability and accuracy of the
results. |
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