Bibliographic record
Abstract
Requirements Engineering (RE) plays an essential role in the software engineering process, being considered as one of the most critical phases of the software development life-cycle. As we might expect, then, the Requirements Engineering would play a similar role in the context of Big Data applications. However, practicing Requirements Engineering is a challenging and complex task. It involves (i) stakeholders with diverse backgrounds and levels of knowledge, (ii) different application domains, (iii) it is expensive and error-prone, (iii) it is important to be aligned with business goals, to name a few. Because it involves such complex activities, a lot has to be understood in order to properly address Requirements Engineering. Especially, when the technology domain (e.g., Big Data) is not yet well explored. In this context, this paper describes a research plan on Requirements Engineering involving the development of Big Data applications. The high-level goal is to investigate: (i) On the technical front, the Requirements Engineering activities with respect to the analysis and specification of Big Data requirements and, (ii) on the management side, the relationship between RE and Business Goals in the development of Big Data Software applications.
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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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".