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

2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering Approaches, 2023.

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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BibTeX


@InProceedings{publications/data-flowgraphs-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 = 	 {2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering Approaches},
  year = 	 {2023},
  url = 	 {/publications/data-flowgraphs-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 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering Approaches
%D 2023	
%F publications/data-flowgraphs-as-complete-causal-graphs
%U /publications/data-flowgraphs-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  - 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering Approaches
DA  - 2023/03/16	
ID  - publications/data-flowgraphs-as-complete-causal-graphs
UR  - /publications/data-flowgraphs-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.. (2023). Dataflow graphs as complete causal graphs. 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering Approaches Available from /publications/data-flowgraphs-as-complete-causal-graphs.html.