International Workshop on Software Measurement (IWSM'01) 137 Montral, Qubec, Canada -- August 28-29, 2001
Bibliographic record
Abstract
This paper describes and illustrates a methodology for identifying the correctness of software functional requirements on the basis of a logic-based dynamic framework. It focuses on the issues related to user and/or system functional requirements; quality attributes, measures and analysis methods, and integrates the core concepts of the Graphical Requirement Analysis (GRA) and COSMIC-FFP techniques:The proposed approach provides a structured procedure for arranging functional software requirements into a graphical framework, thereby providing a means for evaluating their clarity and their presence/absence. Moreover, the architecture of this approach makes it possible to trace specific entities forwards, from system/user requirements to design, and backwards. The way in which the proposed Integrated Measure for Functional Requirements (IMFR) captures critical aspects of functional requirements such as ambiguous or incomplete requirements, incomplete linkages from software requirements to system requirements and to design and/or to test cases is illustrated . Using a sub-system of the Generic Westinghouse Reactor Protection (GWRP) control system case study as an example, we identify and demonstrate various ambiguities of textual software requirements.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.021 |
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".