Management of performance requirements for information systems
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
The management of performance requirements is a major challenge for information systems as well as other software systems. This is because performance requirements can have a global impact on the target system. In addition, there are interactions and trade-offs among performance requirements, other nonfunctional requirements (NFRs), and the numerous alternatives for the target system. To provide a systematic approach to managing performance requirements, this paper presents a performance requirements framework (PeRF). It integrates and catalogues a variety of kinds of knowledge of information systems and performance. These include: performance concepts, software performance engineering principles for building performance into systems, and information systems development knowledge. In addition, layered structures organize performance knowledge and the development process. All this knowledge is represented using an existing goal-oriented approach, the "NFR framework", which offers a developer-directed graphical treatment for stating NFRs, analyzing and interrelating them, and determining the impact of decisions upon NFRs. This approach allows customized solutions to be built, taking into account the characteristics of the particular domain. The use of PeRF in managing performance requirements is illustrated in a study of performance requirements and other NFRs for a university student record system. This paper concludes with a summary of other studies of information systems, tool support and directions for future work.
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 | 0.000 |
| 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 | 0.001 |
| 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