  Regression testing is an essential activity to assure that software code
changes do not adversely affect existing functionalities. With the wide
adoption of Continuous Integration (CI) in software projects, which increases
the frequency of running software builds, running all tests can be
time-consuming and resource-intensive. To alleviate that problem, Test case
Selection and Prioritization (TSP) techniques have been proposed to improve
regression testing by selecting and prioritizing test cases in order to provide
early feedback to developers. In recent years, researchers have relied on
Machine Learning (ML) techniques to achieve effective TSP (ML-based TSP). Such
techniques help combine information about test cases, from partial and
imperfect sources, into accurate prediction models. This work conducts a
systematic literature review focused on ML-based TSP techniques, aiming to
perform an in-depth analysis of the state of the art, thus gaining insights
regarding future avenues of research. To that end, we analyze 29 primary
studies published from 2006 to 2020, which have been identified through a
systematic and documented process. This paper addresses five research questions
addressing variations in ML-based TSP techniques and feature sets for training
and testing ML models, alternative metrics used for evaluating the techniques,
the performance of techniques, and the reproducibility of the published
studies. We summarize the results related to our research questions in a
high-level summary that can be used as a taxonomy for classifying future TSP
studies.
