Applications of symbolic evaluation

Clarke, Lori A.; Richardson, Debra J.

doi:10.1016/0164-1212(85)90004-4

Cited by 77 publications

(42 citation statements)

References 29 publications

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“…On the other hand, in the method that restricts the class of target formulas, the satisfiability of path conditions is determined by linear programming (LP), or by integer programming (IP) [2] if the path condition is constructed with only linear constraints. When LP is used, a path condition is determined to be unsatisfiable if there exists no integer solution that makes the path condition satisfiable.…”

Section: Satisfiability Determination In Ifp Detectionmentioning

confidence: 99%

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Detection of infeasible paths using Presburger arithmetic

Naoi

Takahashi

1999

Syst. Comp. Jpn.

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Section: Satisfiability Determination In Ifp Detectionmentioning

confidence: 99%

“…One involves the application of the automatic theo- rem proving to the path conditions themselves [1,4,5], and the other restricts the class of target formulas and definitively determines the satisfiability of formulas in the class [2,3].…”

Section: Satisfiability Determination In Ifp Detectionmentioning

confidence: 99%

Detection of infeasible paths using Presburger arithmetic

Naoi

Takahashi

1999

Syst. Comp. Jpn.

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“…Using constraint solvers such as Z3 1 or CVC4, 2 one can generate the inputs that would lead the execution to the targeted point. Symbolic execution can be used for many purposes, such as bug detection, program verification, debugging, maintenance, and fault localization [3].…”

Section: Symbolic Executionmentioning

confidence: 99%

Run-time Validation of Behavioral Adaptations

Cardozo

Christophe

Roover

et al. 2014

Proceedings of 6th International Workshop on Context-Oriented Programming - COP'14

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Context-oriented programming enables the composition of behavioral adaptations into a running software system. Behavioral adaptations provide the most appropriate behavior of a system when their contexts are activated or deactivated, according to the situations at hand in the system's execution environment. Behavioral adaptations can be defined by third-party vendors or even be acquired at run time. As the systems grows in complexity it becomes increasingly di cult to reason about every possible runtime adaptation. Therefore, behavioral adaptations that lead to erroneous states or compromise the system's security might be di cult to detect statically. To prevent such undesired behavioral adaptations from happening, we introduce a run-time correctness verification approach. Our approach uses a symbolic execution engine to reason about the reachable states of the system, whenever contexts are activated or deactivated. Context activation and deactivation requests are allowed depending on the presence of erroneous states within reachable states. Our approach is a step forward to ensure the run-time correctness of software systems that adapt their behavior dynamically.

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“…The efficacy of symbolic execution is twofold, one is automatic generation of test inputs and other is high coverage. Symbolic execution can be used for different purposes, such as bug detection, program verification, debugging, maintenance, and fault localization [10].…”

Section: Introductionmentioning

confidence: 99%

Test Image Generation using Segmental Symbolic Evaluation

Jameel¹,

Lin²

2014

IJNDC

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Image processing applications have played a vital role in modern life and they are required to be well tested due to their significance and human dependence on them. Testing of image processing application is difficult due to complex nature of images in terms of their generation and evaluation. The presented technique is first of its type to generate test images based on symbolic evaluation of program under test. The idea is based on the fact that, neighboring image operations are applied by selecting a segment of image pixels called a window, and iterated by sliding window over entire image. We imitate neighboring operations using symbolic values for the pixels rather than concrete values. The path constraint is extracted for each path in the program under test and solved for concrete solutions. Test images are generated based on solution of path constraints for each identified path. We have tested the proposed scheme on different programs and the results show that test images are successfully generated for each path to ensure the path coverage of the program under test and identifying infeasible paths.

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Applications of symbolic evaluation

Cited by 77 publications

References 29 publications

Detection of infeasible paths using Presburger arithmetic

Detection of infeasible paths using Presburger arithmetic

Run-time Validation of Behavioral Adaptations

Test Image Generation using Segmental Symbolic Evaluation

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