Generating Unit Tests from Formal Proofs

Engel, Christian; Hähnle, Reiner

doi:10.1007/978-3-540-73770-4_10

Cited by 50 publications

(41 citation statements)

References 18 publications

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“…Examples of coverage criteria are: statement coverage which requires that each line of the code is executed; loopk which limits to a threshold k the number of times we iterate on loops. The standard approach to generate test cases statically is to perform a symbolic execution of the program [7,8,12,14,17,18,20], where the contents of variables are expressions rather than concrete values. Symbolic execution produces a system of constraints over the input variables consisting of the conditions to execute the different paths.…”

Section: Introductionmentioning

confidence: 99%

Resource-Driven CLP-Based Test Case Generation

Albert

Gómez-Zamalloa

Rojas

2012

Logic-Based Program Synthesis and Transformation

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Abstract. Test Data Generation (TDG) aims at automatically obtaining test inputs which can then be used by a software testing tool to validate the functional behaviour of the program. In this paper, we propose resource-aware TDG, whose purpose is to generate test cases (from which the test inputs are obtained) with associated resource consumptions. The framework is parametric w.r.t. the notion of resource (it can measure memory, steps, etc.) and allows using software testing to detect bugs related to non-functional aspects of the program. As a further step, we introduce resource-driven TDG whose purpose is to guide the TDG process by taking resource consumption into account. Interestingly, given a resource policy, TDG is guided to generate test cases that adhere to the policy and avoid the generation of test cases which violate it.

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Section: Introductionmentioning

confidence: 99%

Resource-Driven CLP-Based Test Case Generation

Albert

Gómez-Zamalloa

Rojas

2012

Logic-Based Program Synthesis and Transformation

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“…-Perhaps the most striking trend is that deductive verification is emerging as a base technology. It is not only employed for correctness proofs, but in automatic test generation [19,34,30,10], and bug finding [50,36]. Among the state of the art efforts is the KeY tool [2], which it is close to complete coverage of the Java programming language [9].…”

Section: Static Verification Of Software Principlesmentioning

confidence: 99%

“…For instance, there is work on discovering likely invariants by dynamic analysis [31] or testing [35], and the proposed framework could well be ideal for dynamic-to-static feedbacks of similar kind. Another issue is the broadening of our current deductive test case generation approach [30] to control related aspects, like call-graph related test coverage criteria.…”

Section: Additional Featuresmentioning

confidence: 99%

A Unified Approach for Static and Runtime Verification: Framework and Applications

Ahrendt

Pace

Schneider

2012

Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change

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Abstract. Static verification of software is becoming ever more effective and efficient. Still, static techniques either have high precision, in which case powerful judgements are hard to achieve automatically, or they use abstractions supporting increased automation, but possibly losing important aspects of the concrete system in the process. Runtime verification has complementary strengths and weaknesses. It combines full precision of the model (including the real deployment environment) with full automation, but cannot judge future and alternative runs. Another drawback of runtime verification can be the computational overhead of monitoring the running system which, although typically not very high, can still be prohibitive in certain settings. In this paper we propose a framework to combine static analysis techniques and runtime verification with the aim of getting the best of both techniques. In particular, we discuss an instantiation of our framework for the deductive theorem prover KeY, and the runtime verification tool Larva. Apart from combining static and dynamic verification, this approach also combines the data centric analysis of KeY with the control centric analysis of Larva. An advantage of the approach is that, through the use of a single specification which can be used by both analysis techniques, expensive parts of the analysis could be moved to the static phase, allowing the runtime monitor to make significant assumptions, dropping parts of expensive checks at runtime. We also discuss specific applications of our approach.

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“…To generate exploits we build on work from test generation [12,16] with symbolic execution. Our implementation outputs the found exploits as JUnit tests such that the test fails if the program is insecure.…”

Section: Introductionmentioning

confidence: 99%

Exploit Generation for Information Flow Leaks in Object-Oriented Programs

Huy

Bubel

Hähnle

2015

ICT Systems Security and Privacy Protection

Self Cite

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Abstract. We present a method to generate automatically exploits for information flow leaks in object-oriented programs. Our approach combines self-composition and symbolic execution to compose an insecurity formula for a given information flow policy and a specification of the security level of the program locations. The insecurity formula gives then rise to a model which is used to generate input data for the exploit. A prototype tool called KEG implementing the described approach for Java programs has been developed, which generates exploits as executable JUnit tests.

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Generating Unit Tests from Formal Proofs

Cited by 50 publications

References 18 publications

Resource-Driven CLP-Based Test Case Generation

Resource-Driven CLP-Based Test Case Generation

A Unified Approach for Static and Runtime Verification: Framework and Applications

Exploit Generation for Information Flow Leaks in Object-Oriented Programs

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