  Software testing is essential for the reliable development of complex
software systems. A key step in software testing is fault localization, which
uses test data to pinpoint failure-inducing combinations for further diagnosis.
Existing fault localization methods, however, are largely deterministic, and
thus do not provide a principled approach for assessing probabilistic risk of
potential root causes, or for integrating domain and/or structural knowledge
from test engineers. To address this, we propose a novel Bayesian fault
localization framework called BayesFLo, which leverages a flexible Bayesian
model on potential root cause combinations. A key feature of BayesFLo is its
integration of the principles of combination hierarchy and heredity, which
capture the structured nature of failure-inducing combinations. A critical
challenge, however, is the sheer number of potential root cause scenarios to
consider, which renders the computation of posterior root cause probabilities
infeasible even for small software systems. We thus develop new algorithms for
efficient computation of such probabilities, leveraging recent tools from
integer programming and graph representations. We then demonstrate the
effectiveness of BayesFLo over state-of-the-art fault localization methods, in
a suite of numerical experiments and in two motivating case studies on the JMP
XGBoost interface.
