Related Experiment Video
Updated: May 29, 2026
10:56
A User-friendly and Powerful R Analysis of Large-scale Datasets
Published on: November 4, 2025
The stack overflow recommendations dataset (SORD) - A large-scale curated dataset of recommendations related stack
Arjumand Fatima1, Onaiza Maqbool1
1Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan.
Data in Brief
|May 28, 2026
Summary
This study mines Stack Overflow discussions to extract software recommendations for developers. The resulting dataset aids in building better software recommendation systems.
Area of Science:
- Empirical Software Engineering
- Data Mining
- Natural Language Processing
Background:
- Developer discussions on Q&A sites contain valuable insights for software recommendations.
- Existing methods for extracting software preferences from these discussions are limited.
- Stack Overflow is a primary source of developer discussions and software-related queries.
Purpose of the Study:
- To empirically explore developer discussions on Stack Overflow for software recommendation insights.
- To create a dataset of software recommendation-related queries, answers, and comments.
- To facilitate the development of automated software recommendation systems for developers.
Main Methods:
- Utilized the Stack Overflow data dump (October 2025).
- Converted XML data to SQL Server database records.
- Applied keyword-based filtering (19 keywords) to identify recommendation-related content in questions, answers, and comments.
Main Results:
- Extracted a significant volume of relevant content: 73.9k questions (title), 1.1M questions (body), 2.2M answers, and 1.9M comments.
- Results increase with substring matching.
- The enriched dataset includes metadata like Users, Badges, Votes, and Tags.
Conclusions:
- The extracted dataset is valuable for empirical software engineering and machine learning research.
- It can be used to train models for recommendation systems.
- The data serves as a benchmark for evaluating software recommendation systems.
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