Controlled Natural Language for Requirements Specification: A Systematic Literature Review
Bibliographic record
Abstract
Requirements are critical artifacts of the software development life-cycle. They express capabilities that the system should provide, guiding both the development and testing process. Given their significance, requirements specification has attracted the interest of researchers and practitioners in recent years. Requirements specification is an activity where requirements are specified, i.e., documented. In this context, Controlled Natural Languages (CNL) were proposed as a compromise between the ambiguity of natural language and the complexity of formal languages. CNLs enable the specification of requirements using accurate statements that can be processed automatically, while remaining understandable by stakeholders. In this article, we perform a Systematic Literature Review (SLR) to identify, categorize, and compare CNL approaches for requirements specification. The SLR covers 133 primary studies published between 2000 and 2024. We evaluate them according to seven dimensions: context, scope, targeted requirements types, specification technique, tool support, validation method, and adoption. We provide a categorization framework that summarizes the evaluated dimensions, and we identify directions for future research. Our main results reveal: (1) four types of CNL: standalone templates, requirement patterns, elementary templates, and linguistic rules, (2) limited support for automated tools and domain vocabulary usage, and (3) lack of validation through case studies and limited adoption for the majority of approaches.
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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 | 0.031 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.033 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".