  Self-Admitted Technical Debt or SATD can be found in various sources, such as
source code comments, commit messages, issue tracking systems, and pull
requests. Previous research has established the existence of relations between
SATD items in different sources; such relations can be useful for investigating
and improving SATD management. However, there is currently a lack of approaches
for automatically detecting these SATD relations. To address this, we proposed
and evaluated approaches for automatically identifying SATD relations across
different sources. Our findings show that our approach outperforms baseline
approaches by a large margin, achieving an average F1-score of 0.829 in
identifying relations between SATD items. Moreover, we explored the
characteristics of SATD relations in 103 open-source projects and describe nine
major cases in which related SATD is documented in a second source, and give a
quantitative overview of 26 kinds of relations.

Software Engineering;Artificial Intelligence