Blaming in component-based real-time systems

Gößler, Gregor; Aştefănoaei, Lăcrămioara

doi:10.1145/2656045.2656048

Cited by 18 publications

(40 citation statements)

References 29 publications

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“…As a result, the faulty components and their events might be blamed just because during their analysis wrong behavior of other components is removed. For our implementation of the algorithm in [6], we observed the same problem. Furthermore, we detected that this approach is not fully correct and can produce different results in some situation than it claims to.…”

Section: Discussionmentioning

confidence: 99%

“…This issue can arise if broadcast channels are used in the timed automata modeling a system's behavior. Additionally, we detected that deadlocks in the networks of timed automata constructed by the algorithm of [6] prevent the proper construction of counterfactual behavior and lead to wrong hypothesized causes. We have verified the existence of this problem during a discussion with the first author of [6].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, we found that the type of input and information a user needs to provide to an algorithm have a significant impact on the applicability. For example, we noted that, the language-based framework of [7] for modeling a system is powerful but quite abstract approach, whereas the usage of timed automata in [6] might be more intuitive. The rules in our policy compliance Gößler and Métayer [7] Gößler and Astefanoaei [6] Policy Compliance Algorithm -Configuration Effort: Languagebased configuration is rather abstract, complex and error-prone.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

ACCBench: A Framework for Comparing Causality Algorithms

Rehwald¹,

Ibrahim²,

Beckers³

et al. 2017

Electron. Proc. Theor. Comput. Sci.

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Modern socio-technical systems are increasingly complex. A fundamental problem is that the borders of such systems are often not well-defined a-priori, which among other problems can lead to unwanted behavior during runtime. Ideally, unwanted behavior should be prevented. If this is not possible the system shall at least be able to help determine potential cause(s) a-posterori, identify responsible parties and make them accountable for their behavior. Recently, several algorithms addressing these concepts have been proposed. However, the applicability of the corresponding approaches, specifically their effectiveness and performance, is mostly unknown. Therefore, in this paper, we propose ACCBench, a benchmark tool that allows to compare and evaluate causality algorithms under a consistent setting. Furthermore, we contribute an implementation of the two causality algorithms by [7] and [6] as well as of a policy compliance approach based on some concepts of [16]. Lastly, we conduct a case study of an Intelligent Door Control System, which exposes concrete strengths and weaknesses of all algorithms under different aspects. In the course of this, we show that the effectiveness of the algorithms in terms of cause detection as well as their performance differ to some extent. In addition, our analysis reports on some qualitative aspects that should be considered when evaluating each algorithm. For example, the human effort needed to configure the algorithm and model the use case is analyzed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

ACCBench: A Framework for Comparing Causality Algorithms

Rehwald¹,

Ibrahim²,

Beckers³

et al. 2017

Electron. Proc. Theor. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…Our line of work [9,18,8,19,7] starts with preliminary definitions of causalities for component-based systems [9,18] and extends to real-time settings for system definitions with logical constraints [19], synchronous systems [8], and timed automata [7]. A salient difference of our work from existing ones is that, although we assume components are black-boxes, we take expected component behaviors specified in component properties as guidelines for trace reconstruction.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach to Causality Analysis

Wang

Geoffroy

Gößler

et al. 2015

Runtime Verification

Self Cite

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In component-based safety-critical systems, when a system safety property is violated, it is necessary to analyze which components are the cause. Given a system execution trace that exhibits component faults leading to a property violation, our causality analysis formalizes a notion of counterfactual reasoning (\what would the system behavior be if a component had been correct?") and algorithmically derives such alternative system behaviors, without re-executing the system itself. In this paper, we show that we can improve precision of the analysis if 1) we can emulate execution of components instead of relying on their contracts, and 2) take into consideration input/output dependencies between components to avoid blaming components for faults induced by other components. We demonstrate the utility of the extended analysis with a case study for a closed-loop patient-controlled analgesia system. Abstract. In component-based safety-critical systems, when a system safety property is violated, it is necessary to analyze which components are the cause. Given a system execution trace that exhibits component faults leading to a property violation, our causality analysis formalizes a notion of counterfactual reasoning ("what would the system behavior be if a component had been correct?") and algorithmically derives such alternative system behaviors, without re-executing the system itself. In this paper, we show that we can improve precision of the analysis if 1) we can emulate execution of components instead of relying on their contracts, and 2) take into consideration input/output dependencies between components to avoid blaming components for faults induced by other components. We demonstrate the utility of the extended analysis with a case study for a closed-loop patient-controlled analgesia system.

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“…In a similar spirit, [23] proposes a methodology for computing shortest counterexamples for symbolic model checking of so-called LTL with past formulae. Other "nice to have" properties of causality such as, compositionality, for instance, were addressed in [10,9,11,12,5].…”

Section: Introductionmentioning

confidence: 99%

Causality for General LTL-definable Properties

Caltais

Guetlein

Leue

2019

Electron. Proc. Theor. Comput. Sci.

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In this paper we provide a notion of causality for the violation of general Linear Temporal Logic (LTL) properties. The current work is a natural extension of the previously proposed approach handling causality in the context of LTL-definable safety properties [20,19]. The major difference is that now, counterexamples of general LTL properties are not merely finite traces, but infinite lasso-shaped traces. We analyze such infinite counterexamples and identify the relevant ordered occurrences of causal events, obtained by unfolding the looping part of the lasso shaped counterexample sufficiently many times. The focus is on LTL properties from practical considerations: the current results are to be implemented in QuantUM, a tool for causality checking, that exploits explicit state LTL model checking.

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Blaming in component-based real-time systems

Cited by 18 publications

References 29 publications

ACCBench: A Framework for Comparing Causality Algorithms

ACCBench: A Framework for Comparing Causality Algorithms

A Hybrid Approach to Causality Analysis

Causality for General LTL-definable Properties

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