  In the process of software evolution, developers often sacrifice the
long-term code quality to satisfy the short-term goals due to specific reasons,
which is called technical debt. In particular, self-admitted technical debt
(SATD) refers to those that were intentionally introduced and remarked by code
comments. Those technical debts reduce the quality of software and increase the
cost of subsequent software maintenance. Therefore, it is necessary to find out
and resolve these debts in time. Recently, many approaches have been proposed
to identify SATD. However, those approaches either have a low accuracy or are
complex to implementation in practice. In this paper, we propose a simple
unsupervised baseline approach that fuzzily matches task annotation tags (MAT)
to identify SATD. MAT does not need any training data to build a prediction
model. Instead, MAT only examines whether any of four task tags (i.e. TODO,
FIXME, XXX, and HACK) appears in the comments of a target project to identify
SATD. In this sense, MAT is a natural baseline approach, which has a good
understandability, in SATD identification. In order to evaluate the usefulness
of MAT, we use 10 open-source projects to conduct the experiment. The
experimental results reveal that MAT has a surprisingly excellent performance
for SATD identification compared with the state-of-the-art approaches. As such,
we suggest that, in the future SATD identification studies, MAT should be
considered as an easy-to-implement baseline to which any new approach should be
compared against to demonstrate its usefulness.
