Assessing Diversity in Creating Seed Set for Snowballing Search for Systematic Literature Review in Software Engineering
Bibliographic record
Abstract
Background: Systematic literature reviews (SLRs) require robust search strategies to ensure comprehensive coverage. Although database searches have traditionally been the primary method, snowballing has emerged as an effective alternative strategy in software engineering research. However, the success of snowballing heavily depends on the initial seed set's composition, particularly regarding diversity across authors, publication years, and venues. Objective: This study investigates how different diversity characteristics in seed set creation influence snowballing performance and effectiveness in identifying relevant literature. Method: We conducted replication studies of two existing SLRs, comparing their conventional seed set creation approaches with our diversity-driven methodology, where we systematically incorporated diversity characteristics into constructing the seed sets. Results: Our diversity-based approach demonstrated substantial improvements, with a precision of 0.019 (compared to 0.006 in the original), a relative recall of 0.97 (versus 0.921), and an F-measure of 0.0372 (improving from 0.0119). Conclusions: The empirical evidence suggests that incorporating diversity criteria in seed set creation enhances snowballing efficacy while maintaining comprehensive coverage of relevant literature. This approach offers a systematic and effective method for conducting snowballbased literature reviews in software engineering research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.446 | 0.771 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.056 | 0.030 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".