Dataflow graphs as complete causal graphs

Andrei Paleyes; Siyuan Guo; Bernhard Schölkopf; Neil D. Lawrence

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Dataflow graphs as complete causal graphs

Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence

2nd International Conference on AI Engineering – Software Engineering for AI.

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.

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Cite this Paper

BibTeX


@InProceedings{publications/dataflow-graphs-as-complete-causal-graphs,
  title = 	 {Dataflow graphs as complete causal graphs},
  author = 	 {Paleyes, Andrei and Guo, Siyuan and Schölkopf, Bernhard and Lawrence, Neil D.},
  booktitle = 	 {2nd International Conference on AI Engineering – Software Engineering for AI},
  url = 	 {/publications/dataflow-graphs-as-complete-causal-graphs.html},
  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.
}
}

Endnote

%0 Conference Paper
%T Dataflow graphs as complete causal graphs
%A Andrei Paleyes
%A Siyuan Guo
%A Bernhard Schölkopf
%A Neil D. Lawrence
%B 2nd International Conference on AI Engineering – Software Engineering for AI	
%F publications/dataflow-graphs-as-complete-causal-graphs
%U /publications/dataflow-graphs-as-complete-causal-graphs.html
%X 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.

RIS


TY  - CPAPER
TI  - Dataflow graphs as complete causal graphs
AU  - Andrei Paleyes
AU  - Siyuan Guo
AU  - Bernhard Schölkopf
AU  - Neil D. Lawrence
BT  - 2nd International Conference on AI Engineering – Software Engineering for AI
DA  - false	
ID  - publications/dataflow-graphs-as-complete-causal-graphs
UR  - /publications/dataflow-graphs-as-complete-causal-graphs.html
AB  - 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.

ER  -

APA


Paleyes, A., Guo, S., Schölkopf, B. & Lawrence, N.D.. (false). Dataflow graphs as complete causal graphs. 2nd International Conference on AI Engineering – Software Engineering for AI Available from /publications/dataflow-graphs-as-complete-causal-graphs.html.

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Andrei Paleyes

Visiting Researcher, Cambridge University

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Neil D. Lawrence

The DeepMind Professor of Machine Learning, Cambridge University

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