Formal Software Requirement Elicitation based on Semantic Algebra and Cognitive Computing
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it