| Abstrak/Abstract |
Developing a software project without the
appropriate amount of effort would significantly impede and
even fail the project, putting the software developer's quality at
risk. Therefore, software development effort estimation (SDEE)
is the most critical activity in software engineering. SDEE has
seen extensive research, resulting in a massive rise in the
literature in a relatively short period. In this regard, it is crucial
to identify the significant study topics in software development
effort estimation that will assist researchers in understanding
and recognizing research trends. This research applied a
systematic literature review (SLR) to compile all journals from
the predefined search directory about software development
effort estimation thoroughly and unbiasedly from 2018 to 2022.
This review was a prelude to further research activities in
software development effort estimation. Five research topics out
of 71 papers have been revealed, including the machine learning
approach, algorithmic technique, expert judgement, dataset
analysis, and evaluation metric. With 27 journals, deploying a
machine learning approach for SDEE is the most discussed
research topic. The potential research described in this study
can be investigated further in software development effort
estimation field. |
