Development of recommendation systems for software engineering: the CROSSMINER experience
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Abstract (arXiv)
To perform their daily tasks, developers intensively make use of existing resources by consulting open-source software (OSS) repositories. Such platforms contain rich data sources, e.g., code snippets, documentation, and user discussions, that can be useful for supporting development activities. Over the last decades, several techniques and tools have been promoted to provide developers with innovative features, aiming to bring in improvements in terms of development effort, cost savings, and productivity. In the context of the EU H2020 CROSSMINER project, a set of recommendation systems has been conceived to assist software programmers in different phases of the development process. The systems provide developers with various artifacts, such as third-party libraries, documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This paper is an experience report to present the knowledge pertinent to the set of recommendation systems developed through the CROSSMINER project. We explain in detail the challenges we had to deal with, together with the related lessons learned when developing and evaluating these systems. Our aim is to provide the research community with concrete takeaway messages that are expected to be useful for those who want to develop or customize their own recommendation systems. The reported experiences can facilitate interesting discussions and research work, which in the end contribute to the advancement of recommendation systems applied to solve different issues in Software Engineering.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Mar 2021.
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Citing papers per year: 2023: 3; 2024: 0; 2025: 1.
4 papers.
- 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
- 2023Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Studycited by 7
- 2025Detecting Malicious Source Code in PyPI Packages with LLMs: Does RAG Come in Handy?cited by 3
- 2023Leveraging Data Mining Algorithms to Recommend Source Code Changescited by 0
- 2025Detecting Malicious Source Code in PyPI Packages with LLMs: Does RAG Come in Handy?18 Apr 2025
- 2023Large Language Models for Software Engineering: A Systematic Literature Review21 Aug 2023
- 2023Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study4 May 2023
- 2023Leveraging Data Mining Algorithms to Recommend Source Code Changes29 Apr 2023