  Software Quality Assurance (SQA) planning aims to define proactive plans,
such as defining maximum file size, to prevent the occurrence of software
defects in future releases. To aid this, defect prediction models have been
proposed to generate insights as the most important factors that are associated
with software quality. Such insights that are derived from traditional defect
models are far from actionable-i.e., practitioners still do not know what they
should do or avoid to decrease the risk of having defects, and what is the risk
threshold for each metric. A lack of actionable guidance and risk threshold can
lead to inefficient and ineffective SQA planning processes. In this paper, we
investigate the practitioners' perceptions of current SQA planning activities,
current challenges of such SQA planning activities, and propose four types of
guidance to support SQA planning. We then propose and evaluate our AI-Driven
SQAPlanner approach, a novel approach for generating four types of guidance and
their associated risk thresholds in the form of rule-based explanations for the
predictions of defect prediction models. Finally, we develop and evaluate an
information visualization for our SQAPlanner approach. Through the use of
qualitative survey and empirical evaluation, our results lead us to conclude
that SQAPlanner is needed, effective, stable, and practically applicable. We
also find that 80% of our survey respondents perceived that our visualization
is more actionable. Thus, our SQAPlanner paves a way for novel research in
actionable software analytics-i.e., generating actionable guidance on what
should practitioners do and not do to decrease the risk of having defects to
support SQA planning.
