Towards a Theory of Interoperability of Software Systems
Bibliographic record
Abstract
Interoperability is a property of software quality that is related to the cooperation between software systems for exchanging information. However, this concept is not well explained or understood. A theory would be useful to explain interoperability in terms of its essential elements and propositions. Theoretical contributions of interoperability are intended to formalize this concept by using common frameworks, models, and meta-models. However, tentative contributions developed in the past have failed to propose a theory of interoperability due to four reasons: (1) a disunified vocabulary is used, (2) the essential elements for describing interoperability are not well identified, (3) only a single level of interoperability is assessed, and (4) interoperability principles are not well formalized. This paper tentatively proposes an axiomatic theory of interoperability as a complementary approach to the existing knowledge. The proposed theory seeks to better formalize the concepts of interoperability and suggest actions aimed at establishing interoperability. After a brief review of related works and the state of the art, a set of axioms and propositions is presented. This theory is evaluated by a group of experts, and an example is presented to illustrate its use. Conclusions and future works are outlined at the end of the paper.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".