Formal Software Requirement Elicitation based on Semantic Algebra and Cognitive Computing
Bibliographic record
Abstract
Autonomous software requirement analysis and generation are a persistent challenge to theories and technologies of software engineering. A cognitive system is demanded to automatically elicit and rigorously refine informal software requirements in natural language descriptions into formal specifications. This paper presents a novel software requirements elicitation methodology based on latest advances in software science and denotational mathematics such as semantic algebra and concept algebra. It is found that user requirements for a software system in natural language may be either expressed in to-be sentences for software structures or to-do sentences for software behaviors. Thus, formal software requirements may be elicited by two sets of structural and functional models. This approach is implemented by a tool for Formal Requirement Elicitation and Analysis (FREA). Experimental results demonstrate that the FREA tool may rigorously elicit and generate formal requirements for arbitrary software systems specified in real-time process algebra (RTPA) or equivalent notations. This technology paves a way towards autonomous code generation in software engineering.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".