An Empirical Investigation to Understand the Difficulties and Challenges of Software Modellers When Using Modelling Tools
Bibliographic record
Abstract
Software modelling is a challenging and error-prone task. Existing Model-Driven Engineering (MDE) tools provide modellers with little aid, partly because tool providers have not investigated users' difficulties through empirical investigations such as field studies. This paper presents the results of a two-phase user study to identify the most prominent difficulties that users might face when developing UML Class and State-Machine diagrams using UML modelling tools. In the first phase, we identified the preliminary modelling challenges by analysing 30 Class and State-Machine models that were previously developed by students as a course assignment. The result of the first phase helped us design the second phase of our user study where we empirically investigated different aspects of using modelling tools: the tools' effectiveness, users' efficiency, users' satisfaction, the gap between users' expectation and experience, and users' cognitive difficulties. Our results suggest that users' greatest difficulties are in (1) remembering contextual information and (2) identifying and fixing errors and inconsistencies.
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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 | 0.037 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".