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.

翻译：背景：众包软件工程通过利用全球在线劳动力为软件从业者提供外包工作。然而，由于可选任务种类繁多，软件从业者难以识别合适的任务。因此，越来越多的研究致力于引入推荐系统来为软件从业者推荐CSE任务。目标：本研究旨在分析现有CSE任务推荐系统，调查其提取的数据、推荐方法、关键优势与局限性、推荐任务类型、推荐中人为因素的使用情况、常用平台以及用于推荐的特征。方法：本系统性文献综述依据Kitchenham和Charters指南进行。我们采用手动与自动检索相结合的策略，未对相关文献的发表时间设限。结果：基于预定义的纳入与排除标准，我们筛选出63篇主要研究进行数据提取、分析与综合。根据数据分析结果，我们将提取的数据按数据来源分为4类，对提出的推荐系统进行分类以构建分类体系，并识别了这些系统的关键优势与局限性。研究结果表明，人为因素在CSE任务推荐中起着重要作用。此外，我们识别出五类常见推荐任务类型、常用平台及其在任务推荐中使用的特征。我们还对未来研究方向提出了建议。结论：本系统性文献综述揭示了CSE任务推荐系统的当前趋势、研究空白及未来研究方向。