A Systematic Literature Review on Task Recommendation Systems for Crowdsourced Software Engineering

TL;DR

The paper investigates how task recommendation systems operate within Crowdsourced Software Engineering by synthesizing 65 peer-reviewed studies. It classifies data sources, recommendation methods, and platform features, and highlights human factors as a central, underexplored dimension. The findings show content-based methods dominate, GitHub as the primary platform, and task types centered on repository contributions and issue handling, while data sparsity and cold-start problems persist. The study offers guidance for future research, including integrating human factors, cross-platform knowledge transfer, standardized evaluation benchmarks, and closer integration with development workflows to enhance real-world applicability.

Abstract

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. 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. 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. We selected 65 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 four 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. This SLR provides insights into current trends, gaps, and future research directions in CSE task recommendation systems

Figures (5)

• Figure 1: Search query
• Figure 2: Phases of study selection process
• Figure 3: The steps of performing thematic analysis
• Figure 4: Publication trends
• Figure 5: Taxonomy for proposed methodologies