JDart: A Dynamic Symbolic Analysis Framework

Luckow, Kasper Søe; Dimjašević, Marko; Giannakopoulou, Dimitra; Howar, Falk; Isberner, Malte; Kahsai, Temesghen; Rakamarić, Zvonimir; Raman, V. V.

doi:10.1007/978-3-662-49674-9_26

Cited by 64 publications

(25 citation statements)

References 24 publications

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“…The initial driver of the development of JDart was the need for an analysis that is robust enough to handle large and complex systems, concretely the Au-toResolver software for prediction and resolution of airplane loss of separation developed at NASA Ames Research Center [7]. Though JDart provides a robust and scalable platform for dynamic symbolic analysis of Java programs [7], we had to extend its functionality in several ways in order to be able to compete at SV-COMP 2020 [1]. We developed:…”

Section: Overviewmentioning

confidence: 99%

JDart: Dynamic Symbolic Execution for Java Bytecode (Competition Contribution)

Mues

Howar

2020

Tools and Algorithms for the Construction and Analysis of Systems

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JDart performs dynamic symbolic execution of Java programs: it executes programs with concrete inputs while recording symbolic constraints on executed program paths. A constraint solver is then used for generating new concrete values from recorded constraints that drive execution along previously unexplored paths. JDart is built on top of the Java PathFinder software model checker and uses the JConstraints library for the integration of constraint solvers.

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Section: Overviewmentioning

confidence: 99%

JDart: Dynamic Symbolic Execution for Java Bytecode (Competition Contribution)

Mues

Howar

2020

Tools and Algorithms for the Construction and Analysis of Systems

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“…Over the last decade, several tools implementing dynamic test generation for various programming languages, properties and application domains have been developed. Examples of such tools are DART [62], EGT [22], PathCrawler [117], CUTE [107], EXE [24], SAGE [64], CatchConv [93], PEX [113], KLEE [23], CREST [20], BitBlaze [111], Splat [87], Apollo [4], YOGI [67], Kudzu [106], S2E [31], and JDart [85]. The above tools differ by how they perform dynamic symbolic execution (for languages such as C, Java, x86, .NET), by the type of constraints they generate (for theories such as linear arithmetic, bit-vectors, arrays, uninterpreted functions, etc.…”

Section: Efficient Constraint Solvingmentioning

confidence: 99%

Automated Software Test Generation: Some Challenges, Solutions, and Recent Advances

Candea

Godefroid

2019

Lecture Notes in Computer Science

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The automation of software testing promises to delegate to machines what is otherwise the most labor-intensive and expensive part of software development. The past decade has seen a resurgence in research interest for this problem, bringing about significant progress. In this article, we provide an overview of automated test generation for software, and then discuss recent developments that have had significant impact on real-life software.

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“…This work is the first work that uses separation logic for concolic testing. The engineering design of our tool is based on that of JDart [32]. However, JDart, like most concolic execution engines, e.g., [18,19,24,33,42], does not support data structures as symbolic input for testing methods.…”

Section: Concolic Testing Programs With Heap Inputsmentioning

confidence: 99%

Concolic Testing Heap-Manipulating Programs

Pham

Phan

et al. 2019

Lecture Notes in Computer Science

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Concolic testing is a test generation technique which works effectively by integrating random testing generation and symbolic execution. Existing concolic testing engines focus on numeric programs. Heap-manipulating programs make extensive use of complex heap objects like trees and lists. Testing such programs is challenging due to multiple reasons. Firstly, test inputs for such programs are required to satisfy non-trivial constraints which must be specified precisely. Secondly, precisely encoding and solving path conditions in such programs are challenging and often expensive. In this work, we propose the first concolic testing engine called CSF for heap-manipulating programs based on separation logic. CSF effectively combines specification-based testing and concolic execution for test input generation. It is evaluated on a set of challenging heapmanipulating programs. The results show that CSF generates valid test inputs with high coverage efficiently. Furthermore, we show that CSF can be potentially used in combination with precondition inference tools to reduce the user effort.

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JDart: A Dynamic Symbolic Analysis Framework

Cited by 64 publications

References 24 publications

JDart: Dynamic Symbolic Execution for Java Bytecode (Competition Contribution)

JDart: Dynamic Symbolic Execution for Java Bytecode (Competition Contribution)

Automated Software Test Generation: Some Challenges, Solutions, and Recent Advances

Concolic Testing Heap-Manipulating Programs

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