mlsanityData to 7 Oct 2026

Successful Combination of Database Search and Snowballing for Identification of Primary Studies in Systematic Literature Studies

arXiv 2307.02612 ↗First posted 5 Jul 2023cs.SEcited by 16

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Abstract (arXiv)

Background: A good search strategy is essential for a successful systematic literature study. Historically, database searches have been the norm, which has later been complemented with snowball searches. Our conjecture is that we can perform even better searches if combining the two search approaches, referred to as a hybrid search strategy. Objective: Our main objective was to compare and evaluate a hybrid search strategy. Furthermore, we compared some alternative hybrid search strategies to assess whether it was possible to identify more cost-efficient ways of searching for relevant primary studies. Method: To compare and evaluate the hybrid search strategy, we replicated an SLR on industry-academia collaboration in software engineering. The SLR used a more traditional approach to searching for relevant articles for an SLR, while the replication was conducted using a hybrid search strategy. Results: In our evaluation, the hybrid search strategy was superior in identifying relevant primary studies. It identified 30 percent more primary studies and even more when focusing only on peer-reviewed articles. To embrace individual viewpoints when assessing research articles and minimise the risk of missing primary studies, we introduced two new concepts, wild cards and borderline articles, when conducting systematic literature studies. Conclusions: The hybrid search strategy is a strong contender for being used when conducting systematic literature studies. Furthermore, alternative hybrid search strategies may be viable if selected wisely in relation to the start set for snowballing. Finally, the two new concepts were judged as essential to cater for different individual judgements and to minimise the risk of excluding primary studies that ought to be included.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jul 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 6; 2026: 5.

16 papers.

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  1. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
  2. 2024Artificial Intelligence for Literature Reviews: Opportunities and Challengescited by 30
  3. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  4. 2024The Promise and Challenges of Using LLMs to Accelerate the Screening Process of Systematic Reviewscited by 14
  5. 2024Large Language Model for Qualitative Research -- A Systematic Mapping Studycited by 4
  6. 2025Generative AI for Testing of Autonomous Driving Systems: A Surveycited by 3
  7. 2025Aggregating empirical evidence from data strategy studies: a case on model quantizationcited by 1
  8. 2025Agile Management for Machine Learning: A Systematic Mapping Studycited by 1
  9. 2025Hyperspectral Anomaly Detection Methods: A Survey and Comparative Studycited by 0
  10. 2025A Multi-Level Strategy for Deepfake Content Moderation under EU Regulationcited by 0
  11. 2025Self-organizing maps for water quality assessment in reservoirs and lakes: A systematic literature reviewcited by 0
  12. 2026On the Use of a Large Language Model to Support the Conduction of a Systematic Mapping Study: A Brief Report from a Practitioner's Viewcited by 0
4 more
  1. 2026Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Makingcited by 0
  2. 2026From Toil to Thought: Designing for Strategic Exploration and Responsible AI in Systematic Literature Reviewscited by 0
  3. 2026Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systemscited by 0
  4. 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systemscited by 0