Towards a requirements engineering artefact model in the context of big data software development projects: Research in progress
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.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".