ACCBench: A Framework for Comparing Causality Algorithms
Simon Rehwald, Amjad Ibrahim, Kristian Beckers, Alexander Pretschner

TL;DR
This paper introduces ACCBench, a benchmarking framework for comparing causality algorithms in socio-technical systems, evaluating their effectiveness, performance, and practical considerations through a detailed case study.
Contribution
It presents ACCBench, a tool for consistent evaluation of causality algorithms, and provides implementations and a case study analyzing their strengths and weaknesses.
Findings
Algorithms vary in effectiveness and performance.
Qualitative factors like human effort are important.
Case study highlights strengths and weaknesses of each algorithm.
Abstract
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…
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