  AI-powered systems have gained widespread popularity in various domains,
including Autonomous Vehicles (AVs). However, ensuring their reliability and
safety is challenging due to their complex nature. Conventional test adequacy
metrics, designed to evaluate the effectiveness of traditional software
testing, are often insufficient or impractical for these systems. White-box
metrics, which are specifically designed for these systems, leverage neuron
coverage information. These coverage metrics necessitate access to the
underlying AI model and training data, which may not always be available.
Furthermore, the existing adequacy metrics exhibit weak correlations with the
ability to detect faults in the generated test suite, creating a gap that we
aim to bridge in this study.
  In this paper, we introduce a set of black-box test adequacy metrics called
"Test suite Instance Space Adequacy" (TISA) metrics, which can be used to gauge
the effectiveness of a test suite. The TISA metrics offer a way to assess both
the diversity and coverage of the test suite and the range of bugs detected
during testing. Additionally, we introduce a framework that permits testers to
visualise the diversity and coverage of the test suite in a two-dimensional
space, facilitating the identification of areas that require improvement.
  We evaluate the efficacy of the TISA metrics by examining their correlation
with the number of bugs detected in system-level simulation testing of AVs. A
strong correlation, coupled with the short computation time, indicates their
effectiveness and efficiency in estimating the adequacy of testing AVs.
