Abstract
The detection and understanding of reasons for defects and inadvertent behavior in software is challenging due to its ever increasing complexity. One major aspect contributing to this complexity is the multitude of features a user might select from in configurable systems. In this article, we tackle this challenge by introducing the notion of feature causality that identifies features and their interactions which are the reasons for a system showing certain functional and non-functional properties seen as effects. Feature causality operates at the level of system configurations and is based on counterfactual reasoning, inspired by the seminal definition of actual causality by Halpern and Pearl. Towards turning feature causality into meaningful explanations for the reasons why an effect emerges, we present various explication methods, e.g., by cause–effect covers, quantifications of causal impacts based on notions like responsibility and blame, causal reasoning with uncertainty, and feature interactions. Through a close connection of feature causality to prime implicants, we derive 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 impact of features on effect properties, and detect feature interactions.
Original language | English |
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Article number | 111915 |
Number of pages | 19 |
Journal | Journal of Systems and Software |
Volume | 209 |
DOIs | |
Publication status | Published - Mar 2024 |
Funding
The authors are supported by the DFG, Germany through the Collaborative Research Center TRR 248 (see https://perspicuous-computing.science , project ID 389792660) and the Cluster of Excellence EXC 2050/1 (CeTI, project ID 390696704, as part of Germany’s Excellence Strategy).
Funders | Funder number |
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Center for Evolutionary and Theoretical Immunology | 390696704 |
Deutsche Forschungsgemeinschaft | 389792660, EXC 2050/1 |
Keywords
- Causality
- Configurable systems
- Feature interactions
- Formal methods
- Root causes
- Variability