[2303.09552] Dataflow graphs as complete causal graphs
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Computer Science > Software Engineering

arXiv:2303.09552 (cs)
[Submitted on 16 Mar 2023]

Title:Dataflow graphs as complete causal graphs

Authors:Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence
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Abstract:Component-based development is one of the core principles behind modern software engineering practices. Understanding of causal relationships between components of a software system can yield significant benefits to developers. Yet modern software design approaches make it difficult to track and discover such relationships at system scale, which leads to growing intellectual debt. In this paper we consider an alternative approach to software design, flow-based programming (FBP), and draw the attention of the community to the connection between dataflow graphs produced by FBP and structural causal models. With expository examples we show how this connection can be leveraged to improve day-to-day tasks in software projects, including fault localisation, business analysis and experimentation.
Comments: Accepted to 2nd International Conference on AI Engineering - Software Engineering for AI (CAIN 23)
Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2303.09552 [cs.SE]
  (or arXiv:2303.09552v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2303.09552

Submission history

From: Andrei Paleyes [view email]
[v1] Thu, 16 Mar 2023 17:59:13 UTC (1,224 KB)
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