Improving Quality of Software Requirements by Using a Triplet Structure
Bibliographic record
Abstract
The development of a quality software system is a priority in the domain of software engineering. Quality requires to define unambiguous and consistent requirements that are not conducive to various interpretations. Indeed, the success of the realization of a software system depends largely on the phase of software requirements specification. The requirements specification phase consists, among other things, in describing in a precise and unambiguous manner the characteristics of the system to be developed. Moreover, the techniques for writing software specification documents used in the industry don't facilitate to define unambiguous and consistent requirements. In industry, software requirements are often written in natural language, and no technical details are specified. Thus, software requirements are incomplete, inconsistent and prone to ambiguities, and therefore interpretation errors can easily be made by analysts. This article introduces a new technique for writing and developing software requirements that could help to reduce the ambiguities and inconsistencies in the document of specification software requirements. Our technique is validated by the development of a new tool that detects the ambiguities and inconsistencies in the software requirements, and generates the potential methods and classes from requirements written in natural language. Our tool integrates a set of techniques in natural language processing (NLP), and in artificial intelligence which helps to improve the software requirements quality.
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.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".