Towards a requirements engineering artefact model in the context of big data software development projects: Research in progress
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
There is ample literature that suggests that the field of Big Data is growing rapidly. Also, there is emerging literature on the need to create end-user Big Data applications, as distinct from “data analytics” that typically employs machine learning algorithms to find value in large datasets for the stakeholder. A solid foundation for creating sound applications is a thorough understanding of domain and artefact models that embody artefact types and activities involved in a software project. This paper focuses on the Requirements Engineering (RE) aspect of a Big Data software project. Currently, there are no known RE artefact models to support RE process design and project understanding. To fill this void, this paper proposes a RE artefact model for Big Data end-user applications (BD-REAM). The paper also describes a method for creating the artefact model, including the basic elements and inter-relationships involved in the model.
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 | 0.005 | 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 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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