Towards Integrating Scenario-Based Requirements Engineering for Autonomous Vehicle Systems
Bibliographic record
Abstract
In the ever-evolving landscape of software engineering, the success of a project largely depends on precise and well-understood requirements. The scenario-based approach in requirement engineering stands out for its ability to bridge the gap between abstract requirements and real-world applications where diverse and constantly evolving requirements are common. This position paper introduces the concept of scenario-based approach and its potential in facilitating the development of complex systems. This position paper uses autonomous vehicles as a case study to illustrate the proposed approach’s feasibility. Developing complex systems like an autonomous vehicles system presents unique challenges in requirements engineering, with a primary issue being effective stakeholder involvement and alignment of expectations with the final outcome. We proposed a multi-step scenario-based approach to maximize stakeholders’ satisfaction while minimizing conflict. These steps include scenario identification, scenario analysis, scenario validation, and iterative refinement. This paper posits the potential of scenario-based requirements engineering to provide a structured approach to addressing system needs by defining requirements within the context of real-world scenarios, thereby enhancing stakeholder participation. Furthermore, the paper identifies key areas where this requirement engineering approach can be enhanced to improve dynamic usability and efficiency of complex systems such as autonomous vehicles.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".