Year: 2020
Type: conference-paper
Abstract: Understanding the root cause of a defect is critical to isolating and repairing buggy behavior. We present Causal Testing, a new method of root-cause analysis that relies on the theory of counterfactual causality to identify a set of executions that likely hold key causal information necessary to understand and repair buggy behavior. Using the Defects4J benchmark, we find that Causal Testing could be applied to 71% of real-world defects, and for 77% of those, it can help developers identify the root cause of the defect. A controlled experiment with 37 developers shows that Causal Testing improves participants' ability to identify the cause of the defect from 80% of the time with standard testing tools to 86% of the time with Causal Testing. The participants report that Causal Testing provides useful information they cannot get using tools such as JUnit. Holmes, our prototype, open-source Eclipse plugin implementation of Causal Testing, is available at http://holmes.cs.umass.edu/.
Authors: Brittany Johnson, Yuriy Brun, Alexandra Meliou
Institution: University of Massachusetts Amherst
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
Awards: CAREER: Reverse Data Management: Reverse-Engineering Data Transformations to Understand, Diagnose, and Manipulate Data, EAGER: Exploring the Feasibility of Software Testing Techniques to Evaluate Fairness Algorithms in Software Systems, CAREER: Improving Software Quality using Dynamically Inferred Models
OpenAlex ID: W2889801262