Bibliographic record
Abstract
Abstract Computer systems programming was first developed by those expected to benefit from the system, i.e., the “users.” Therefore, the need to communicate system “requirements” was minimal. As the computer industry developed, hardware became increasingly more complex, powerful, and extensive, resulting in an increased demand for computer software. Therefore, programming became the purview of separate organizations specializing in software development. It is generally accepted that difficulties in discovering and documenting software requirements has become the major source of software development problems. Software engineering was introduced as a method of applying a more rigorous approach to the development of computer systems. Although software engineering has many facets, the basic components of software engineering were and still are software requirements, software design, implementation ( coding ), and testing . As the field matured, many of the simpler software engineering components became engineering fields in their own right, for example: software requirements engineering, configuration management, software quality assurance, and project management. In turn, software requirements engineering consists of requirements elicitation, requirements analysis, requirements specifications, requirements verification , and requirements management . Software requirements elicitation is the process through which the acquirers (customers, buyers, or users) and suppliers (developers or contractors) of a software system discover, review, articulate, and understand a requirement. Concept of operations ( ConOps ) analysis and the ConOps document are the premier tool of the requirements elicitation procession . Concept analysis and development of a ConOps document helps assists users and acquirers with the clarification of user needs and requirements in addition to easing the problems of communication among users, acquirers, and developers. ConOps provide a bridge from user needs and acquirers requirements to the system development process. Guidelines for concept analysis and development of a ConOps document are presented.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".