Applications of Causality in Software Testing: A Rapid Review

2026-06-16 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A rapid review published in June 2026, titled "Applications of Causality in Software Testing," systematically analyzed 27 studies applying causal reasoning to software testing activities like debugging, fairness assessment, and performance evaluation. The review organizes the literature using a four-layered causal inference pipeline: causal representation, structure discovery, identification, and effect estimation. Findings indicate a research concentration on identification and estimation, with representation and discovery techniques remaining underexplored in testing contexts. The analysis also highlights cross-layer challenges, including model misspecification, untested assumptions, and limited empirical evaluation, which collectively hinder practical application. This structured perspective aims to unify disparate contributions and guide future empirical and methodological work, proposing a research agenda to address identified gaps.

Key takeaway

For research scientists exploring advanced software testing methodologies, recognize that current causal inference applications concentrate on result interpretation and debugging. You should prioritize developing robust methods for causal representation and structure discovery, as these areas are significantly underexplored. Focus on addressing challenges like model misspecification and limited empirical validation to enhance the practical impact and scalability of causal testing techniques in real-world systems.

Key insights

Causal inference in software testing is fragmented, with key areas like representation and discovery underexplored, limiting practical impact.

Principles

• Causal inference distinguishes cause-effect from correlation.
• Causal models require adequate representation and structural stability.

Method

Causal inference in software testing follows a pipeline: causal representation, structure discovery, identification (e.g., backdoor criterion, interventions), and effect estimation (e.g., regression adjustment, DML).

In practice

• Apply causal reasoning for debugging, fairness, and performance evaluation.
• Integrate causal inference into test design and result interpretation.

Topics

• Causal Inference
• Software Testing
• Causal Modeling
• Debugging
• Performance Evaluation
• Fairness Assessment

Best for: AI Scientist, Research Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.