Modeling and Reasoning about Software Systems Containing Uncertainty and Variability
Bibliographic record
Abstract
Summary form only given. When building large software-intensive systems, engineers need to express and reason about at least two different types of choices. One type concerns uncertainty - choosing between different design alternatives, resolving inconsistencies, or resolving conflicting stakeholder requirements. Another type deals with variability - supporting different variants of software that serve multiple customers or market segments. Partial modeling has been proposed as a technique for managing uncertainty within a software model. A partial model explicates points of uncertainty and represents the set of possible models that could be obtained by making decisions and resolving the uncertainty. Methods for reasoning about the entire set of possibilities, transforming the entire set and uncertainty-reducing refinements have recently been developed. Software product line engineering approaches propose techniques for managing the variability within sets of related software product variants. Such approaches explicate points of variability (a.k.a.features) and relationships between them in an artifact usually referred to as a feature model. A selection of features from this model guides the derivation of a specific product of a software product line (SPL). Techniques for reasoning about sets of SPL products, transforming the entire SPL and supporting their partial configuration have recently been developed. Partial models and SPL representations are naturally quite similar - both provide ways of encoding and managing sets of artifacts. The techniques for representing, reasoning with and manipulating these sets, naturally, have much in common. Yet, the goals for creating these product sets are quite different, and thus the two techniques lead to distinct methodological considerations. Uncertainty is an aspect of the development process itself; it is transient and must be reduced and eventually eliminated as knowledge is gathered and decisions are made. Thus, the ultimate goal of resolving uncertainty is to produce only one desired artifact. On the other hand, variability is an aspect of the artifacts simultaneously managed through the entire development process; it is to be preserved and carefully engineered to represent the desired range of product variants required. Thus, product lines aim to produce and simultaneously manage multiple artifacts. In this talk, I will survey approaches to representing, reasoning with and transforming models with uncertainty and variability, separately, as well as discuss current work on trying to combine the two 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".