Bibliographic record
Abstract
One of the most essential parts of any software requirements analysis effort is the exploration of alternative ways by which stakeholder problems can be solved. Systematic modeling and analysis of requirements variability allows better decision making during the early requirements phase and substantiates design choices pertaining to the configurability aspect of the system-to-be. This thesis proposes the use of goal models for capturing and reasoning about requirements variability. The goal models we adopt consist of AND/OR decompositions of stakeholder goals and express alternative ways by which stakeholders may wish to achieve them. By capturing goal variability using such models, we propose a shift of focus from variability of the software design, to variability of the problem that the design is intended to solve. This way, we ensure that every important variation of the problem is identified and analyzed before variations of the solution are specified. \n \nThe thesis exploits opportunities that arise from this new viewpoint. Firstly, a variability-intensive goal decomposition process is proposed. The process is based on associating each high-level goal to a set of variability concerns that must be addressed through decomposition. We introduce a universal categorization of such concerns and also show how domain-specific variability concerns can be identified by annotating domain corpora. Concern-driven decomposition offers a structured way of thinking about problem variability, while systematizing its identification process. Further, an expressive LTL-based preference language is introduced to support leverage of large spaces of goal alternatives. The language allows the expression of preferences over behavioral and qualitative properties of solutions and a reasoning tool allows the identification of alternatives that satisfy these preferences. This way, individual stakeholders can get the solution that exactly fits their needs in a particular situation, through simply specifying desired high-level characteristics of these solutions. Finally, a framework for connecting alternatives at the goal level to alternative configurations of common desktop applications is presented. The framework shows how a vast number of configurations of a software application can be evaluated and ranked with respect to a small number of quality goals that are more intuitive to and comprehensible by end users.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".