A Refinement Calculus for Requirements Engineering based on Argumentation Theory: Tool and Additional Material
Bibliographic record
Abstract
The Requirements Engineering (RE) process starts with initial requirements elicited from stakeholders – however conflicting, unattainable, incomplete and ambiguous – and iteratively refines them into a specification that is consistent, complete, valid and unambiguous. That specification consists of functions, quality constraints and assumptions on the environment of the system- to-be. We propose a novel RE process in the form of a calculus called CaRE (Calculus for Requirements Engineering) where the process is envisioned as an iterative application of refinement operators, with each operator removing a defect from the current requirements. Our proposal is motivated by the dialectic and incremental nature of RE activities. The calculus casts the RE problem as an iterative argument between stakeholders, who point out defects (ambiguity, incompleteness, etc.) of existing requirements, and then propose refinements to address those defects, thus leading to the construction of a refinement graph. This graph is then a conceptual model of an RE process enactment. The semantics of these models is provided by Argumentation Theory, where a requirement may be attacked for having a defect, which in turn may be eliminated by a refinement. This package includes additional material associated with the paper that proposes CaRE titled "A Refinement Calculus for Requirements Engineering based on Argumentation Theory", accepted to the 39th International Conference on Conceptual Modeling (ER 2020). The package includes the following files: 1. TechRepCaRE.pdf: a technical report with the description of a sample application of CaRE 2. tool.jar: jar file of the tool that implements CaRE. Requires Java SE Development Kit 9 to run 2. Instructions: textual instructions on how to run the tool that implements CaRE 3. Syntax Instructions: textual instructions about the syntax accepted by the tool 4. exampleApplicationScenario.pl: scenario input file for the case described in TechRepCaRE.pdf 5. exampleCalculus.pl: example input file for the case described in the submitted paper 6. erpaper.pdf: preprint of the ER 2020 Paper titled "A Refinement Calculus for Requirements Engineering based on Argumentation Theory" that refers to the current technical report.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.066 | 0.024 |
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".