  Statistical analysis is the tool of choice to turn data into information, and
then information into empirical knowledge. To be valid, the process that goes
from data to knowledge should be supported by detailed, rigorous guidelines,
which help ferret out issues with the data or model, and lead to qualified
results that strike a reasonable balance between generality and practical
relevance. Such guidelines are being developed by statisticians to support the
latest techniques for Bayesian data analysis. In this article, we frame these
guidelines in a way that is apt to empirical research in software engineering.
  To demonstrate the guidelines in practice, we apply them to reanalyze a
GitHub dataset about code quality in different programming languages. The
dataset's original analysis (Ray et al., 2014) and a critical reanalysis
(Berger at al., 2019) have attracted considerable attention -- in no small part
because they target a topic (the impact of different programming languages) on
which strong opinions abound. The goals of our reanalysis are largely
orthogonal to this previous work, as we are concerned with demonstrating, on
data in an interesting domain, how to build a principled Bayesian data analysis
and to showcase some of its benefits. In the process, we will also shed light
on some critical aspects of the analyzed data and of the relationship between
programming languages and code quality.
  The high-level conclusions of our exercise will be that Bayesian statistical
techniques can be applied to analyze software engineering data in a way that is
principled, flexible, and leads to convincing results that inform the state of
the art while highlighting the boundaries of its validity. The guidelines can
support building solid statistical analyses and connecting their results, and
hence help buttress continued progress in empirical software engineering
research.
