Iterative, Interactive Analysis of Agent-goal Models for Early Requirements Engineering
Bibliographic record
Abstract
Conceptual modeling allows abstraction, communication and consensus building in system development. It is challenging to expand and improve the accuracy of models in an iterative process, producing models able to facilitate analysis. Modeling and analysis can be especially challenging in early Requirements Engineering (RE), where high-level system requirements are discovered. In this stage, hard-to-measure non-functional requirements are critical; understanding the interactions between systems and stakeholders is a key to system success. Goal models have been introduced as a means to ensure stakeholder needs are met in early RE. Because of the high-level, social nature of early RE models, it is important to provide procedures which prompt stakeholder involvement (interaction) and model improvement (iteration). Most current approaches to goal model analysis require quantitative or formal information that is hard to gather in early RE, or produce analysis results automatically over models. Approaches are needed which balance automated analysis over complex models with the need for interaction and iteration.\nThis work develops a framework for iterative, interactive analysis for early RE using agent-goal models. We survey existing approaches for goal model analysis, providing guidelines using domain characteristics to advise on procedure selection. We define requirements for an agent-goal model framework specific to early RE analysis, using these requirements to evaluate the appropriateness of existing work and to motivate and evaluate the components of our analysis framework. We provide a detailed review of forward satisfaction procedures, exploring how different model interpretations affect analysis results. A survey of agent-goal variations in practice is used to create a formal definition of the i* modeling framework which supports sensible syntax variations. This definition is used to precisely define analysis procedures and concepts throughout the work. The framework consists of analysis procedures, implemented in the OpenOME requirements modeling tool, which allow users to ask âWhat if?â and âIs this goal achievable, and how?â questions. Visualization techniques are introduced to aid analysis understanding. Consistency checks are defined over the interactive portion of the framework. Implementation, performance and potential optimizations are described. Group and individual case studies help to validate framework effectiveness in practice. Contributions are summarized in light of the requirements for early RE analysis. Finally, limitations and future work are described.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".