Applications of Causality and Causal Inference in Software Engineering

Patrick Chadbourne, Nasir U. Eisty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Causal inference is a study of causal relationships between events and the statistical study of inferring these relationships through interventions and other statistical techniques. Causal reasoning is any line of work toward determining causal relationships, including causal inference. This paper explores the relationship between causal reasoning and various fields of software engineering. This paper aims to uncover which software engineering fields are currently benefiting from the study of causal inference and causal reasoning, as well as which aspects of various problems are best addressed using this methodology. With this information, this paper also aims to find future subjects and fields that would benefit from this form of reasoning and to provide that information to future researchers. This paper follows a systematic literature review, including; the formulation of a search query, inclusion and exclusion criteria of the search results, clarifying questions answered by the found literature, and synthesizing the results from the literature review. Through close examination of the 45 found papers relevant to the research questions, it was revealed that the majority of causal reasoning as related to software engineering is related to testing through root cause localization. Furthermore, most causal reasoning is done informally through an exploratory process of forming a Causality Graph as opposed to strict statistical analysis or introduction of interventions. Finally, causal reasoning is also used as a justification for many tools intended to make the software more human-readable by providing additional causal information to logging processes or modeling languages.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023
EditorsYeong-Tae Song, Junghwan Rhee, Yuseok Jeon
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages47-52
Number of pages6
ISBN (Electronic)9798350345889
DOIs
StatePublished - 2023
Event21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023 - Orlando, United States
Duration: 23 May 202325 May 2023

Publication series

NameProceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023

Conference

Conference21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023
Country/TerritoryUnited States
CityOrlando
Period23/05/2325/05/23

Keywords

  • causal inference
  • causal reasoning
  • causality graph
  • software engineering
  • systematic literature review

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