Requirements Engineering Visualization: A Systematic Literature Review
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
Requirements Engineering (RE) is a decision-centric activity which is highly data-intensive. The results of this process are known to have key impact on the results of the project. As known from the experience in other fields and disciplines, visualization can potentially provide more insights into data, information and knowledge studied. While research in the area of information visualization and its application to software engineering has rapidly increased over the last decade, there is only a limited amount of studies addressing the usage and impact of visualization techniques for RE activities. In this paper, we report on the results of a Systematic Literature Review (SLR) related to RE visualization. Extending the established SLR process by the usage of grounded theory for the encoding of papers, we synthesize 18 usage patterns. Even though there are punctual applications, there is a clear deficit on a holistic perspective across the different RE activities. As another conclusion, we derive the clear need for more research on visualization support in particular for tackling requirements uncertainty, requirements verification, and modeling, as well as non-functional requirements (NFRs).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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