A Engenharia de Requisitos: Um caso de implementação de um sistema para engenharia de requisitos.
Bibliographic record
Abstract
A implementação de softwares que atenda as expectativas dos usuários, em relação às funcionalidades desejadas, custos e prazos ainda é um processo complexo. O alto índice de falhas na construção de sistemas representa perda de qualidade e elevação de custos aos desenvolvedores, comprometendo os resultados perante o cliente-usuário. Como ferramenta para evitar esse cenário, esta investigação exploratória, utilizando a metodologia de apresentação de produto, trata sobre a implementação de um software de engenharia de requisitos, denominado SER, demonstrando as fases de construção e testes diante dos pressupostos teóricos que embasam as etapas a serem cumpridas na identificação e acompanhamento de aspectos essenciais à adequada elaboração de projetos de softwares. Confirmou-se a aplicabilidade do produto elaborado, o qual representa uma solução adequada ao atendimento das necessidades do projetista/analista de ferramentas de apoio para a etapa de projeto e levantamento de requisitos, pois cumpre os aspectos imprescindíveis recomendados pelo International Requirements Engineering Board (IREB).
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.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".