Agile Testing: Past, Present, and Future -- Charting a Systematic Map of Testing in Agile Software Development
Bibliographic record
Abstract
Testing has been a cornerstone of agile software development methodologies since early in the history of the field. However, the terminology used to describe the field - as well as the evidence in existing literature - is largely inconsistent. In order to better structure our understanding of the field and to guide future work, we conducted a systematic mapping of agile testing. We investigated five research questions: which authors are most active in agile testing; what is agile testing used for; what types of paper tend to be published in this field; how do practitioners and academics contribute to research in this field; and what tools are used to conduct agile testing? Of particular interest is our investigation into the source of these publications, which indicates that academics and practitioners focus on different types of publication and, disturbingly, that the number of practitioner papers in the sources we searched is strongly down since 2010.
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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 | 0.027 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".