  Context: Crowdsourced Software Engineering CSE offers outsourcing work to
software practitioners by leveraging a global online workforce. However these
software practitioners struggle to identify suitable tasks due to the variety
of options available. Hence there have been a growing number of studies on
introducing recommendation systems to recommend CSE tasks to software
practitioners. Objective: The goal of this study is to analyze the existing CSE
task recommendation systems, investigating their extracted data, recommendation
methods, key advantages and limitations, recommended task types, the use of
human factors in recommendations, popular platforms, and features used to make
recommendations. Method: This SLR was conducted according to the Kitchenham and
Charters guidelines. We used both manual and automatic search strategies
without putting any time limitation for searching the relevant papers. Results:
We selected 63 primary studies for data extraction, analysis, and synthesis
based on our predefined inclusion and exclusion criteria. From the results of
the data analysis, we classified the extracted data into 4 categories based on
the data extraction source, categorized the proposed recommendation systems to
fit into a taxonomy, and identified the key advantages and limitations of these
systems. Our results revealed that human factors play a major role in CSE task
recommendation. Further we identified five popular task types recommended,
popular platforms, and their features used in task recommendation. We also
provided recommendations for future research directions. Conclusion: This SLR
provides insights into current trends gaps and future research directions in
CSE task recommendation systems.
