Relational Reasoning via SMT Solving

Ghazi, Aboubakr Achraf El; Taghdiri, Mana

doi:10.1007/978-3-642-21437-0_12

Cited by 32 publications

(33 citation statements)

References 14 publications

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“…In accordance with [28], where Z3 is used as a complement to the Alloy solver for proving instances unsatisfiable, the Z3 solver was able to detect almost instantaneously the unsatisfiability of the generated constraints. As a consequence, the SMT-Lib version of the tool was able to detect properly and efficiently the infeasible path prefixes in the control-flow graphs of the methods from groups one and two.…”

Section: Evaluating the Alloy And Z3 Solvers For Relational Symbolic mentioning

confidence: 91%

“…If the constraint system produced for a given path has no solution, this means that the path is infeasible. As the produced constraints are written in a quantified logic that is not generally decidable [28], it can happen that for a given path the solver may neither be able to find a solution for the generated constraint system, nor be able to establish that such a solution does not exist. This is coherent with the problem being not computable in general.…”

Section: Algorithm Inputs and Outputsmentioning

confidence: 99%

“…It will then generate symbols and constraints describing the input content of each of these tables. The used modelling is inspired by the one proposed in [28] for relational types. First, a symbol is created (1) to represent the initial set of objects in each table.…”

Section: Execution Examplementioning

confidence: 99%

“…In order to do so, the modelling proposed in [28] for constraining the content and cardinality of relations, in a way so that the constraints can be effectively solved by Z3, is used. Following [28], new constraints are added (1) to define a function shelves1InvertedList which is the inverse of shelves1List.…”

Section: Execution Examplementioning

confidence: 99%

“…Recently, it has been proposed [28] to translate Alloy constraints into the SMT-Lib standard language [29], as some solvers based on this language, like Microsoft Z3 [30], enable detecting the unsatisfiability of the constraints, which is not possible within the Alloy framework. However, [28] advises to continue using the Alloy analyser for finding 4…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Relational symbolic execution of SQL code for unit testing of database programs

Marcozzi

Vanhoof

Hainaut

2015

Science of Computer Programming

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Symbolic execution is a technique enabling the automatic generation of test inputs that exercise a set of execution paths within a code unit to be tested. If the paths cover a sufficient part of the code under test, the test data offer a representative view of the actual behaviour of this code. This notably enables detecting errors and correcting faults. Relational databases are ubiquitous in software, but symbolic execution of code units that manipulate them remains a non-trivial problem, particularly because of the complex structure of such databases and the complex behaviour of SQL statements. Finding errors in such code units is yet critical, as it can avoid corrupting important data. In this work, we define a symbolic execution translating database manipulation code directly into constraints and integrate it with a more traditional symbolic execution of normal program code. The database tables are represented by relational symbols and the SQL statements by relational constraints over these symbols. An algorithm based on these principles is presented for the symbolic execution of simple Java methods that implement transactional use cases by reading and writing in a relational database, the latter subject to data integrity constraints. The algorithm is integrated in a test generation tool and experimented over sample code. The target language for the constraints produced by the tool is the SMT-Lib standard and the used solver is Microsoft Z3. The results show that the proposed approach enables generating meaningful test data, including valid database content, in reasonable time. In particular, the Z3 solver is shown to be more scalable than the Alloy solver, used in our previous work, for solving relational constraints.

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Section: Evaluating the Alloy And Z3 Solvers For Relational Symbolic mentioning

confidence: 91%

Section: Algorithm Inputs and Outputsmentioning

confidence: 99%

Section: Execution Examplementioning

confidence: 99%

Section: Execution Examplementioning

confidence: 99%