Package-oriented software engineering: a generic architecture
Bibliographic record
Abstract
New methodologies and better techniques are the rule in software engineering, and users of large and complex methodologies benefit greatly from specialized software support tools. However, developing such tools is both difficult and expensive, because developers must implement a lot of functionality in a short time. A promising solution is component-based software development, in particular package-oriented programming (POP). POP fails, however, to satisfy all the requirements of large, complex software engineering tasks. A more generic POP architecture would better serve the development of software engineering environments for large and complex methodologies. Such an architecture emerged from our development experiences with two software engineering research tools: Holmes, a domain analysis support tool; and Egidio, a unified-modeling-language-based business modeling tool. We found this particular architecture simple to understand, easy to implement, and a natural candidate for a generic POP architecture. Our generic architecture satisfies the additional requirements we deem important for larger, more complex software engineering activities. Our experiences show that the strength of this architecture lies in its simplicity and ability to work with multiple users and quickly integrate a wide variety of applications. It is not perfect, but we present it as a first step toward a more general package-oriented architecture to encourage further research in this area.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".