From Checking to Inference: Actual Causality Computations as Optimization Problems

Ibrahim, Amjad; Pretschner, Alexander

doi:10.1007/978-3-030-59152-6_19

Cited by 11 publications

(10 citation statements)

References 21 publications

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“…However, tractable instances such as the Boolean case have been identified by Eiter and Lukasiewicz [38]. For deciding whether a partial interpretation is an actual cause in the Boolean case, Ibrahim and Pretschner presented an approach based on SAT solving [48]. To compute all causes, their implementation relies on checking causality for all possible partial interpretations, suffering from an additional exponential blowup in the number of variables, which we avoid within our approach using prime implicant computations.…”

Section: Related Workmentioning

confidence: 99%

Causality in Configurable Software Systems

Dubslaff,

Weis,

Baier

et al. 2022

Preprint

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Detecting and understanding reasons for defects and inadvertent behavior in software is challenging due to their increasing complexity. In configurable software systems, the combinatorics that arises from the multitude of features a user might select from adds a further layer of complexity. We introduce the notion of feature causality, which is based on counterfactual reasoning and inspired by the seminal definition of actual causality by Halpern and Pearl. Feature causality operates at the level of system configurations and is capable of identifying features and their interactions that are the reason for emerging functional and non-functional properties. We present various methods to explicate these reasons, in particular well-established notions of responsibility and blame that we extend to the feature-oriented setting. Establishing a close connection of feature causality to prime implicants, we provide algorithms to effectively compute feature causes and causal explications. By means of an evaluation on a wide range of configurable software systems, including community benchmarks and real-world systems, we demonstrate the feasibility of our approach: We illustrate how our notion of causality facilitates to identify root causes, estimate the effects of features, and detect feature interactions.

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Section: Related Workmentioning

confidence: 99%

Causality in Configurable Software Systems

Dubslaff,

Weis,

Baier

et al. 2022

Preprint

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“…There, the set of feature configurations with observable effect is obtained by configurable systems analysis, e.g., through family-based verification [32,112,38,28]. Exploiting the Boolean case of HP causality [40,69], those partial feature configurations can be determined where the corresponding systems all show the effect (see AC1), for which there is a reconfiguration that does not exhibit the effect (AC2), and that are minimal (AC3).…”

Section: :6mentioning

confidence: 99%

From Verification to Causality-Based Explications (Invited Talk)

Baier¹,

Dubslaff²,

Funke³

et al. 2021

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In view of the growing complexity of modern software architectures, formal models are increasingly used to understand why a system works the way it does, opposed to simply verifying that it behaves as intended. This paper surveys approaches to formally explicate the observable behavior of reactive systems. We describe how Halpern and Pearl's notion of actual causation inspired verificationoriented studies of cause-effect relationships in the evolution of a system. A second focus lies on applications of the Shapley value to responsibility ascriptions, aimed to measure the influence of an event on an observable effect. Finally, formal approaches to probabilistic causation are collected and connected, and their relevance to the understanding of probabilistic systems is discussed.

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“…Since we assumed the model to be binary, there is only one other thing the agents could have done, namely whatever makes their "measurement" 𝑡𝑟𝑢𝑒. 23 So, we can change the value of 𝑈 24 in the model and see if the result changes.…”

Section: Actual Causalitymentioning

confidence: 99%

“…For ways to automatically check these models, see[23][24][25] 22. In this example we ignore any uncertainty.…”

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confidence: 99%

Designing Accountable Systems

Kacianka,

Pretschner

2021

Preprint

Self Cite

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Accountability is an often called for property of technical systems. It is a requirement for algorithmic decision systems, autonomous cyber-physical systems, and for software systems in general. As a concept, accountability goes back to the early history of Liberalism and is suggested as a tool to limit the use of power. This long history has also given us many, often slightly differing, definitions of accountability. The problem that software developers now face is to understand what accountability means for their systems and how to reflect it in a system's design. To enable the rigorous study of accountability in a system, we need models that are suitable for capturing such a varied concept. In this paper, we present a method to express and compare different definitions of accountability using Structural Causal Models. We show how these models can be used to evaluate a system's design and present a small use case based on an autonomous car. CCS CONCEPTS• Software and its engineering → Designing software; • Computer systems organization → Architectures.

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From Checking to Inference: Actual Causality Computations as Optimization Problems

Cited by 11 publications

References 21 publications

Causality in Configurable Software Systems

Causality in Configurable Software Systems

From Verification to Causality-Based Explications (Invited Talk)

Designing Accountable Systems

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