Agile Testing: A Systematic Mapping across Three Conferences: Understanding Agile Testing in the XP/Agile Universe, Agile, and XP Conferences
Bibliographic record
Abstract
Unit and acceptance testing are central to agile software development, but is that all there is to agile testing? We build on previous work to provide a systematic mapping of agile testing publications at major agile conferences. The analysis presented in this paper allows us to answer research questions like: what is agile testing used for, what types of studies on agile testing have been published, what problems do people have when performing agile testing, and what benefits do these publications offer? We additionally explore topics such as: who are the major authors in this field, in which countries do these authors work, what tools are mentioned, and is the field driven by academics, practitioners, or collaborations? This paper presents our analysis of these topics in order to better structure future work in the field of agile testing and to provide a better understanding of what this field actually entails.
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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 | 0.031 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.034 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".