TLTk: A Toolbox for Parallel Robustness Computation of Temporal Logic Specifications

Cralley, Joseph; Spantidi, Ourania; Hoxha, Bardh; Fainekos, Georgios

doi:10.1007/978-3-030-60508-7_22

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…The PSY-TaLiRo toolbox supports multiple robustness computation libraries, referred to as backends by providing a uniform interface implemented as the Specification class. It is important to note that even though PSY-TaLiRo currently supports TLTk [8] and RTAMT [19], PSY-TaLiRo's modular architecure allows the user to utilize any other robustness computation engine, or, in general, any other reward or cost function. To construct a specification, a user must provide a system requirement written in STL, a dictionary structure specifying the requirement data, and an enumeration value specifying the back-end to use for evaluation.…”

Section: Specificationsmentioning

confidence: 99%

“…On the other hand, RTAMT supports past-time operators and dense time semantics. [8] and RTAMT [19] syntax supported in PSY-TaLiRo.…”

Section: Specificationsmentioning

confidence: 99%

“…It is a fully modular and extensible toolbox for temporal logic guided falsification which mirrors the S-TaLiRo [5] structure. Namely, the users can easily call different temporal logic robustness computation engines (e.g., TLTk [8], RTAMT [19]), optimizers (SciPy), and Systems under Test (SUT) while still offering a common interface and specification language syntax.…”

Section: Introductionmentioning

confidence: 99%

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PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systems

Thibeault¹,

Anderson²,

Chandratre³

et al. 2021

Preprint

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In this paper, we present the Python package PSY-TaLiRo which is a toolbox for temporal logic robustness guided falsification of Cyber-Physical Systems (CPS). PSY-TaLiRo is a completely modular toolbox supporting multiple temporal logic offline monitors as well as optimization engines for test case generation. Among the benefits of PSY-TaLiRo is that it supports search-based test generation for many different types of systems under test. All PSY-TaLiRo modules can be fully modified by the users to support new optimization and robustness computation engines as well as any System under Test (SUT).

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Section: Specificationsmentioning

confidence: 99%

“…On the other hand, RTAMT supports past-time operators and dense time semantics. [8] and RTAMT [19] syntax supported in PSY-TaLiRo.…”

Section: Specificationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systems

Thibeault¹,

Anderson²,

Chandratre³

et al. 2021

Preprint

Self Cite

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“…To run the Part-X function on falsification benchmarks, we developed an SBTG Python package 1 which implements a similar architecture to the S-Taliro [57] and BREACH [58] Matlab tools. Our SBTG tool uses the Python packages RTAMT [28] and TLTk [29] for computing the robustness of STL specifications. The tool also supports a number of optimization functions besides Part-X.…”

Section: Automated Requirement Falsificationmentioning

confidence: 99%

“…In fact, the only assumption we need on f is that it is a locally continuous function. In order to evaluate our approach, we have built an SBTG library in Python (to be publicly released after publication) that can use the RTAMT [28] or TLTk [29] temporal logic robustness Python libraries. We demonstrate our framework on selected functions from the optimization literature and on the F16 benchmark problem [30].…”

Section: Introductionmentioning

confidence: 99%

Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees

Pedrielli¹,

Khandait²,

Surdeep³

et al. 2021

Preprint

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Requirements driven search-based testing (also known as falsification) has proven to be a practical and effective method for discovering erroneous behaviors in Cyber-Physical Systems. Despite the constant improvements on the performance and applicability of falsification methods, they all share a common characteristic. Namely, they are best-effort methods which do not provide any guarantees on the absence of erroneous behaviors (falsifiers) when the testing budget is exhausted. The absence of finite time guarantees is a major limitation which prevents falsification methods from being utilized in certification procedures. In this paper, we address the finite-time guarantees problem by developing a new stochastic algorithm. Our proposed algorithm not only estimates (bounds) the probability that falsifying behaviors exist, but also it identifies the regions where these falsifying behaviors may occur. We demonstrate the applicability of our approach on standard benchmark functions from the optimization literature and on the F16 benchmark problem.

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PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systems

Thibeault

Anderson

Chandratre

et al. 2021

Lecture Notes in Computer Science

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TLTk: A Toolbox for Parallel Robustness Computation of Temporal Logic Specifications

Cited by 13 publications

References 28 publications

PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systems

PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systems

Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees

PSY-TaLiRo: A Python Toolbox for Search-Based Test Generation for Cyber-Physical Systems

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