Characterizing UX Evaluation in Software Modeling Tools: A Literature Review
Bibliographic record
Abstract
Model-Based Software Engineering (MBSE) has a high potential to play a critical role in the whole process of software engineering, bringing many benefits to all stakeholders, yet it is not used by most software developers today, due to both lack of tool capabilities and poor user experience (UX) of tools. This study aims to understand the evaluation types and methods applied by researchers when studying UX in modeling tools (modeling experience or MX) and the types of issues uncovered in these studies. We conducted a literature review using a snowballing approach to gather all studies of this topic. A total of 41 research papers were reviewed. Data extraction was performed based on research questions and a categorization of discussed issues was presented. Several gaps and future opportunities were identified and discussed, which include 1) utilizing interview method in research design; 2) distributing testing tasks based on user profiles; 3) involving UX experts in analysis; 4) scalability testing using large models; 5) assessing MX in areas other than just usability and utility; 6) considering collaborative modeling as an important factor contributing to MX; 7) considering both language issues and tool issues in UX evaluation of software modeling tools. 8) improving the taxonomy of MX challenges; 9) triangulating using multiple methods; and 10) developing and validating MX tool design heuristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".