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Causal Modeling, Discovery, & Inference for Software Engineering
Conference proceeding

Causal Modeling, Discovery, & Inference for Software Engineering

Rick Kazman, Robert Stoddard, David Danks, Yuanfang Cai and IEEE
2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C)
May 2017
DOI: https://doi.org/10.1109/ICSE-C.2017.138

Abstract

Algorithm design and analysis causal inference Computer bugs Conferences Correlation correlation studies empirical software engineering Software Software algorithms Software engineering
This causal discovery analysis is intended as an initial step, and is certainly not the final word. For example, one could apply multiple causal discovery algorithms to measure the sensitivity of the learned structures to the use of the PC algorithm. Moreover, software projects exhibit significant dynamics over time, as code is written, refined, refactored, and so forth. We used static datasets that provide snapshots of the projects at particular moments in time. If we collect longitudinal data about similar variables, then we could start to uncover the underlying causal dynamics. One might also suspect that those dynamics could shift over time, as the software practices and philosophies change, as project members enter and leave, etc. Longitudinal data could also enable us to test for this type of causal non-stationarity. The key point that we have established here, however, is the first demonstration of the applicability and usefulness of causal discovery algorithms applied to observational software engineering datasets.

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4 citations in Web of Science
4 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Software Engineering
Engineering, Electrical & Electronic
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