Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The transition from an informal requirements specification in natural language to a structured, precise specification is an important challenge in practice. It is particularly so for object-oriented methods, defined in the context of the OMG's Model Driven Architecture (MDA), where a key step is to transition from a use case model to an analysis model. However, providing automated support for this transition is challenging, mostly because, in practice, requirements are expressed in natural language and are much less structured than other kinds of development artifacts. Such an automated transformation would enable at least the generation of an initial, likely incomplete, analysis model and enable automated traceability from requirements to code, through various intermediate models. In this article, we propose a method and a tool called aToucan, building on existing work, to automatically generate a UML analysis model comprising class, sequence and activity diagrams from a use case model and to automatically establish traceability links between model elements of the use case model and the generated analysis model. Note that our goal is to save effort through automated support, not to replace human abstraction and decision making. Seven (six) case studies were performed to compare class (sequence) diagrams generated by aToucan to the ones created by experts, Masters students, and trained, fourth-year undergraduate students. Results show that aToucan performs well regarding consistency (e.g., 88% class diagram consistency) and completeness (e.g., 80% class completeness) when comparing generated class diagrams with reference class diagrams created by experts and Masters students. Similarly, sequence diagrams automatically generated by aToucan are highly consistent with the ones devised by experts and are also rather complete, for instance, 91% and 97% message consistency and completeness, respectively. Further, statistical tests show that aToucan significantly outperforms fourth-year engineering students in this respect, thus demonstrating the value of automation. We also conducted two industrial case studies demonstrating the applicability of aToucan in two different industrial domains. Results showed that the vast majority of model elements generated by aToucan are correct and that therefore, in practice, such models would be good initial models to refine and augment so as to converge towards to correct and complete analysis models. A performance analysis shows that the execution time of aToucan (when generating class and sequence diagrams) is dependent on the number of simple sentences contained in the use case model and remains within a range of a few minutes. Five different software system descriptions (18 use cases altogether) were performed to evaluate the generation of activity diagrams. Results show that aToucan can generate 100% complete and correct control flow information of activity diagrams and on average 85% data flAow information completeness. Moreover, we show that aToucan outperforms three commercial tools in terms of activity diagram generation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it