  Real-world software applications must constantly evolve to remain relevant.
This evolution occurs when developing new applications or adapting existing
ones to meet new requirements, make corrections, or incorporate future
functionality. Traditional methods of software quality control involve software
quality models and continuous code inspection tools. These measures focus on
directly assessing the quality of the software. However, there is a strong
correlation and causation between the quality of the development process and
the resulting software product. Therefore, improving the development process
indirectly improves the software product, too. To achieve this, effective
learning from past processes is necessary, often embraced through post mortem
organizational learning. While qualitative evaluation of large artifacts is
common, smaller quantitative changes captured by application lifecycle
management are often overlooked. In addition to software metrics, these smaller
changes can reveal complex phenomena related to project culture and management.
Leveraging these changes can help detect and address such complex issues.
  Software evolution was previously measured by the size of changes, but the
lack of consensus on a reliable and versatile quantification method prevents
its use as a dependable metric. Different size classifications fail to reliably
describe the nature of evolution. While application lifecycle management data
is rich, identifying which artifacts can model detrimental managerial practices
remains uncertain. Approaches such as simulation modeling, discrete events
simulation, or Bayesian networks have only limited ability to exploit
continuous-time process models of such phenomena. Even worse, the accessibility
and mechanistic insight into such gray- or black-box models are typically very
low. To address these challenges, we suggest leveraging objectively [...]
