Causality in configurable software systems

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Contributors

Abstract

Detecting and understanding reasons for defects and inadvertent behavior in software is challenging due to their increasing complexity. In configurable software systems, the combinatorics that arises from the multitude of features a user might select from adds a further layer of complexity. We introduce the notion of feature causality, which is based on counterfactual reasoning and inspired by the seminal definition of actual causality by Halpern and Pearl. Feature causality operates at the level of system configurations and is capable of identifying features and their interactions that are the reason for emerging functional and non-functional properties. We present various methods to explicate these reasons, in particular well-established notions of responsibility and blame that we extend to the feature-oriented setting. Establishing a close connection of feature causality to prime implicants, we provide algorithms to effectively compute feature causes and causal explications. By means of an evaluation on a wide range of configurable software systems, including community benchmarks and real-world systems, we demonstrate the feasibility of our approach: We illustrate how our notion of causality facilitates to identify root causes, estimate the effects of features, and detect feature interactions.

Details

Original languageEnglish
Title of host publicationICSE '22: Proceedings of the 44th International Conference on Software Engineering
PublisherAssociation for Computing Machinery (ACM), New York
Pages325-337
Number of pages13
ISBN (Print)978-1-4503-9221-1
Publication statusPublished - 5 Jul 2022
Peer-reviewedYes

Conference

TitleInternational Conference on Software Engineering
Abbreviated titleICSE'2022
Conference number
Duration8 - 27 May 2022
Website
Degree of recognitionInternational event
Location
CityPittsburgh
CountryUnited States

External IDs

Scopus 85133510076

Keywords

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