  To complete tasks faster, developers often have to sacrifice the quality of
the software. Such compromised practice results in the increasing burden to
developers in future development. The metaphor, technical debt, describes such
practice. Prior research has illustrated the negative impact of technical debt,
and many researchers investigated how developers deal with a certain type of
technical debt. However, few studies focused on the removal of different types
of technical debt in practice.
  To fill this gap, we use the introduction and removal of different types of
self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an
example. This is because deep learning frameworks are some of the most
important software systems today due to their prevalent use in life-impacting
deep learning applications. Moreover, the field of the development of different
deep learning frameworks is the same, which enables us to find common behaviors
on the removal of different types of technical debt across projects. By mining
the file history of these frameworks, we find that design debt is introduced
the most along the development process. As for the removal of technical debt,
we find that requirement debt is removed the most, and design debt is removed
the fastest. Most of test debt, design debt, and requirement debt are removed
by the developers who introduced them. Based on the introduction and removal of
different types of technical debt, we discuss the evolution of the frequencies
of different types of technical debt to depict the unresolved sub-optimal
trade-offs or decisions that are confronted by developers along the development
process. We also discuss the removal patterns of different types of technical
debt, highlight future research directions, and provide recommendations for
practitioners.
