The problems with eclipse modeling tools
Bibliographic record
Abstract
Eclipse offers a wide range of tools supporting various aspects of modeling and Model-Driven Engineering (MDE). Arguably, the Eclipse ecosystem has been and continues to be one of the most important modeling tool repositories and sources of information about these tools, with, for example, more than 180,000 posts in the modeling forums since 2002. In this paper, we collect and analyze the content of the 30 most widely used Eclipse forums associated with different modeling and MDE tools, such as EMF, Xtext, ATL, Epsilon, and GMF. Using state-of-the-art text mining techniques coupled with manual analysis, we explore these forums with respect to two important questions: What are the primary issues, problems, and challenges raised in the use of these tools? And, perhaps even more important: Which of these issues are most commonly faced by "newbies" in the MDE community? Our study provides supporting evidence for some commonly held but unproven beliefs, such that plug-ins and documentation issues are the most common, and suggests which issues actually present the biggest "barriers to entry" for new users of MDE tools, and how they might be addressed.
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 | 0.099 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.024 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".