  With modern requirements, there is an increasing tendency of considering
multiple objectives/criteria simultaneously in many Software Engineering (SE)
scenarios. Such a multi-objective optimization scenario comes with an important
issue -- how to evaluate the outcome of optimization algorithms, which
typically is a set of incomparable solutions (i.e., being Pareto non-dominated
to each other). This issue can be challenging for the SE community,
particularly for practitioners of Search-Based SE (SBSE). On one hand,
multi-objective optimization could still be relatively new to SE/SBSE
researchers, who may not be able to identify the right evaluation methods for
their problems. On the other hand, simply following the evaluation methods for
general multi-objective optimization problems may not be appropriate for
specific SE problems, especially when the problem nature or decision maker's
preferences are explicitly/implicitly available. This has been well echoed in
the literature by various inappropriate/inadequate selection and
inaccurate/misleading use of evaluation methods. In this paper, we first carry
out a systematic and critical review of quality evaluation for multi-objective
optimization in SBSE. We survey 717 papers published between 2009 and 2019 from
36 venues in seven repositories, and select 95 prominent studies, through which
we identify five important but overlooked issues in the area. We then conduct
an in-depth analysis of quality evaluation indicators/methods and general
situations in SBSE, which, together with the identified issues, enables us to
codify a methodological guidance for selecting and using evaluation methods in
different SBSE scenarios.
