  Context: Profiling developers is challenging since many factors, such as
their skills, experience, development environment and behaviors, may influence
a detailed analysis and the delivery of coherent interpretations.
  Objective: We aim at profiling software developers by mining their software
development process. To do so, we performed a controlled experiment where, in
the realm of a Python programming contest, a group of developers had the same
well-defined set of requirements specifications and a well-defined sprint
schedule. Events were collected from the PyCharm IDE, and from the Mooshak
automatic jury where subjects checked-in their code.
  Method: We used n-gram language models and text mining to characterize
developers' profiles, and process mining algorithms to discover their overall
workflows and extract the correspondent metrics for further evaluation.
  Results: Findings show that we can clearly characterize with a coherent
rationale most developers, and distinguish the top performers from the ones
with more challenging behaviors. This approach may lead ultimately to the
creation of a catalog of software development process smells.
  Conclusions: The profile of a developer provides a software project manager a
clue for the selection of appropriate tasks he/she should be assigned. With the
increasing usage of low and no-code platforms, where coding is automatically
generated from an upper abstraction layer, mining developer's actions in the
development platforms is a promising approach to early detect not only
behaviors but also assess project complexity and model effort.
