Automated Support for Searching and Selecting Evidence in Software Engineering: A Cross-domain Systematic Mapping
Bibliographic record
Abstract
Context: Searching and selecting relevant evidence is crucial to answer research questions from secondary studies in Software Engineering (SE). The activities of search and selection of studies are labour-intensive, time-consuming and demand automation support. Objective: Our goal is to identify and summarize the state-of-the-art on automation support for searching and selecting evidence for secondary studies in SE. Method: We performed a systematic mapping on existing automating support to search and select evidence for secondary studies in SE, expanding our investigation in a cross-domain study addressing advancements from the medical field. Results: Our results show that the SE field has a variety of tools and Text Classification (TC) approaches to automate the search and selection activities. However, medicine has more well-established tools with a larger adoption than SE. Cross-validation and experiment are the most adopted methods to assess TC approaches. Furthermore, recall and precision are the most adopted assessment metrics. Conclusion: Automated approaches for searching and selecting studies in SE have not been applied in practice by SE researchers. Integrated and easy-to-use automated approaches addressing consolidated TC techniques can bring relevant advantages on workload and time saving for SE researchers who conduct secondary studies.
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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 | 0.246 | 0.535 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.068 | 0.032 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".