  Software applications have been playing an increasingly important role in
various aspects of society. In particular, mobile apps and web apps are the
most prevalent among all applications and are widely used in various industries
as well as in people's daily lives. To help ensure mobile and web app quality,
many approaches have been introduced to improve app GUI testing via automated
exploration. Despite the extensive effort, existing approaches are still
limited in reaching high code coverage, constructing high-quality models, and
being generally applicable. Reinforcement learning-based approaches are faced
with difficult challenges, including effective app state abstraction, reward
function design, etc. Moreover, they heavily depend on the specific execution
platforms, thus leading to poor generalizability and being unable to adapt to
different platforms.
  We propose PIRLTest, an effective platform-independent approach for app
testing. It utilizes computer vision and reinforcement learning techniques in a
novel, synergistic manner for automated testing. It extracts the GUI widgets
from GUI pages and characterizes the corresponding GUI layouts, embedding the
GUI pages as states. The app GUI state combines the macroscopic perspective and
the microscopic perspective, and attaches the critical semantic information
from GUI images. This enables PIRLTest to be platform-independent and makes the
testing approach generally applicable on different platforms. PIRLTest explores
apps with the guidance of a curiosity-driven strategy, which uses a Q-network
to estimate the values of specific state-action pairs to encourage more
exploration in uncovered pages without platform dependency. The exploration
will be assigned with rewards for all actions, which are designed considering
both the app GUI states and the concrete widgets, to help the framework explore
more uncovered pages.
