Causal fault localisation in dataflow systems

2023/04/24 by Andrei Paleyes, Paleyes, Andrei, Neil D. Lawrence +1 · 1 citation
Computer Science · #Advanced Software Engineering Methodologies #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Model-Driven Software Engineering Techniques #Software Engineering (cs.SE) #Software Engineering Research

paper · pdf · doi:10.48550/arxiv.2304.11987

openalex publication_date 2023/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-driven application development. One of the characteristic features of dataflow computing is the natural access to the dataflow graph of the entire system. Recently it has been observed that these dataflow graphs can be treated as complete graphical causal models, opening opportunities to apply causal inference techniques to dataflow systems. In this demonstration paper we aim to provide the first practical validation of this idea with a particular focus on causal fault localisation. We provide multiple demonstrations of how causal inference can be used to detect software bugs and data shifts in multiple scenarios with three modern dataflow engines.

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