Automated demarcation of requirements in textual specifications: a machine learning-based approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable writing and markup conventions, one can provide an immediate and unequivocal demarcation of requirements at the time a specification is being developed. However, neither the presence nor a fully accurate enforcement of such conventions is guaranteed. The result is that, in many practical situations, analysts end up resorting to after-the-fact reviews for sifting requirements from other material in a requirements specification. This is both tedious and time-consuming. We propose an automated approach for demarcating requirements in free-form requirements specifications. The approach, which is based on machine learning, can be applied to a wide variety of specifications in different domains and with different writing styles. We train and evaluate our approach over an independently labeled dataset comprised of 33 industrial requirements specifications. Over this dataset, our approach yields an average precision of 81.2% and an average recall of 95.7%. Compared to simple baselines that demarcate requirements based on the presence of modal verbs and identifiers, our approach leads to an average gain of 16.4% in precision and 25.5% in recall. We collect and analyze expert feedback on the demarcations produced by our approach for industrial requirements specifications. The results indicate that experts find our approach useful and efficient in practice. We developed a prototype tool, named DemaRQ, in support of our approach. To facilitate replication, we make available to the research community this prototype tool alongside the non-proprietary portion of our training data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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