  Context: Predicting software project effort from Use Case Points (UCP) method
is increasingly used among researchers and practitioners. However, unlike other
effort estimation domains, this area of interest has not been systematically
reviewed. Aims: There is a need for a systemic literature review to provide
directions and supports for this research area of effort estimation.
Specifically, the objective of this study is twofold: to classify UCP effort
estimation papers based on four criteria: contribution type, research approach,
dataset type and techniques used with UCP; and to analyze these papers from
different views: estimation accuracy, favorable estimation context and impact
of combined techniques on the accuracy of UCP. Method: We used the systematic
literature review methodology proposed by Kitchenham and Charters. This
includes searching for the most relevant papers, selecting quality papers,
extracting data and drawing results. Result: The authors of UCP research paper,
are generally not aware of previous published results and conclusions in the
field of UCP effort estimation. There is a lack of UCP related publications in
the top software engineering journals. This makes a conclusion that such papers
are not useful for the community. Furthermore, most articles used small numbers
of projects which cannot support generalizing the conclusion in most cases.
Conclusions: There are multiple research directions for UCP method that have
not been examined so far such as validating the algebraic construction of UCP
based on industrial data. Also, there is a need for standard automated tools
that govern the process of translating use case diagram into its corresponding
UCP metrics. Although there is an increase interest among researchers to
collect industrial data and build effort prediction models based on machine
learning methods, the quality of data is still subject to debate
