review-article

Applications of statistical causal inference in software engineering

Author:

Julien SiebertAuthors Info & Claims

Volume 159, Issue C

https://doi.org/10.1016/j.infsof.2023.107198

Published: 01 July 2023 Publication History

Abstract

Context:

The aim of statistical causal inference (SCI) methods is to estimate causal effects from observational data (i.e., when randomized controlled trials are not possible). In this context, Pearl’s framework based on causal graphical models is an approach that has recently gained popularity and allows for explicit reasoning about issues related to spurious correlations.

Objective:

Our primary goal is to understand to which extend and how Pearl’s graphical framework is applied in software engineering (SE).

Methods:

We performed a systematic mapping study and analysed a total of 25 papers published between 2010 and 2022.

Results:

Our results show that the application of Pearl’s SCI framework in SE is relatively recent and that the corresponding research community is fragmented. Most of the selected papers focus on software quality analysis. There is no clear and widespread community of practice (yet) on how to implement and evaluate SCI in SE.

Conclusions:

To the best of our knowledge this is the first time such a mapping study is done. We believe that SE practitioners might benefit from such a work, as it both provides an overview of the work and people involved in the application of causal inference methods, but also outlines the potential and limitations of such approaches.

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cover image Information and Software Technology

Information and Software Technology Volume 159, Issue C

Jul 2023

242 pages

ISSN:0950-5849

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Elsevier B.V.

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Butterworth-Heinemann

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Published: 01 July 2023

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Graf-Vlachy LWagner S(2024)Cleaning Up Confounding: Accounting for Endogeneity Using Instrumental Variables and Two-Stage ModelsACM Transactions on Software Engineering and Methodology10.1145/367473033:8(1-31)Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1145/3674730
Paleyes ALi HLawrence NBosch JLewis GCleland-Huang JMuccini H(2024)Can causality accelerate experimentation in software systems?Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI10.1145/3644815.3644985(280-281)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3644815.3644985
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