The Philosophy Behind IRES, an Intentional Requirements Engineering Strategy
Bibliographic record
Abstract
Intentionality is considered particularly important in several facets of social context.For example, police investigation usually starts based on "the why", which is searching for a motive.The search for a motive is also frequent in the anamnesis process in medicine, as well as in investigative journalism.In a criminal investigation, early discovery of motive usually provides a track to identify a crime's perpetrators.When in the doctor's office, the usual first question a patient has to answer is why he/she is there.In the same way, no one disagrees that software utility is the backbone of construction success.Since the task of discovering "the why" (goals) is abstract, subjective, and complicated, we delineate a thinking process frame, a philosophy, for guiding Requirements Engineers into focusing on intentions for the elicitation of goals.The philosophy, at the beginning of the IRES (Intentional Requirements Engineering Strategy), provides a backbone to the requirements process.It is composed of four topics (Necessity, Motivation, Goal, Action), and is linked by the intentionality and their interconnections in a given State of Affairs.The goal of this paper is to explain how this frame helps the construction of well-anchored models.
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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 | 0.044 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".