The Requirements Problem for Adaptive Systems
Bibliographic record
Abstract
Requirements Engineering (RE) focuses on eliciting, modeling, and analyzing the requirements and environment of a system-to-be in order to design its specification. The design of the specification, known as the Requirements Problem (RP), is a complex problem-solving task because it involves, for each new system, the discovery and exploration of, and decision making in a new problem space. A system is adaptive if it can detect deviations between its runtime behavior and its requirements, specifically situations where its behavior violates one or more of its requirements. Given such a deviation, an Adaptive System uses feedback mechanisms to analyze these changes and decide, with or without human intervention, how to adjust its behavior as a result. We are interested in defining the Requirements Problem for Adaptive Systems (RPAS). In our case, we are looking for aconfigurablespecification such that whenever requirements fail to be fulfilled, the system can go through a series of adaptations that change its configuration and eventually restore fulfilment of the requirements. From a theoretical perspective, this article formally shows the fundamental differences between standard RE (notably Zave and Jackson [1997]) and RE for Adaptive Systems (see the seminal work by Fickas and Feather [1995], to Letier and van Lamsweerde [2004], and up to Whittle et al. [2010]). The main contribution of this article is to introduce the RPAS as a new RP class that is specific to Adaptive Systems. We relate the RPAS to RE research on the relaxation of requirements, the evaluation of their partial satisfaction, and the monitoring and control of requirements, all topics of particular interest in research on adaptive systems [de Lemos et al. 2013]. From an engineering perspective, we define a proto-framework for solving RPAS, which illustrates features needed in future frameworks for adaptive software systems.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".