Feature causality

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.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.</p