A Mapping Study on Requirements Engineering in Agile Software Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Agile software development (ASD) methods have gained popularity in the industry and been the subject of an increasing amount of academic research. Although requirements engineering (RE) in ASD has been studied, the overall understanding of RE in ASD as a phenomenon is still weak. We conducted a mapping study of RE in ASD to review the scientific literature. 28 articles on the topic were identified and analyzed. The results indicate that the definition of agile RE is vague. The proposed benefits from agile RE included lower process overheads, a better requirements understanding, a reduced tendency to over allocate development resources, responsiveness to change, rapid delivery of value, and improved customer relationships. The problematic areas of agile RE were the use of customer representatives, the user story requirements format, the prioritization of requirements, growing technical debt, tacit requirements knowledge, and imprecise effort estimation. We also report proposed solutions to the identified problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it