  Context: Families of experiments (i.e., groups of experiments with the same
goal) are on the rise in Software Engineering (SE). Selecting unsuitable
aggregation techniques to analyze families may undermine their potential to
provide in-depth insights from experiments' results.
  Objectives: Identifying the techniques used to aggregate experiments' results
within families in SE. Raising awareness of the importance of applying suitable
aggregation techniques to reach reliable conclusions within families.
  Method: We conduct a systematic mapping study (SMS) to identify the
aggregation techniques used to analyze families of experiments in SE. We
outline the advantages and disadvantages of each aggregation technique
according to mature experimental disciplines such as medicine and pharmacology.
We provide preliminary recommendations to analyze and report families of
experiments in view of families' common limitations with regard to joint data
analysis.
  Results: Several aggregation techniques have been used to analyze SE families
of experiments, including Narrative synthesis, Aggregated Data (AD), Individual
Participant Data (IPD) mega-trial or stratified, and Aggregation of p-values.
The rationale used to select aggregation techniques is rarely discussed within
families. Families of experiments are commonly analyzed with unsuitable
aggregation techniques according to the literature of mature experimental
disciplines.
  Conclusion: Data analysis' reporting practices should be improved to increase
the reliability and transparency of joint results. AD and IPD stratified appear
to be suitable to analyze SE families of experiments.
