Reverse Engineering Research Should Target Cooperative Information System Requirements
Bibliographic record
Abstract
One of the premises of this panel is that the Information Systems (ISs) of tomorrow will be component-based and distributed. Until recently, ISs were designed to support single functions (e.g., purchase orders or payroll). Brodie argues convincingly that future ISs are to support business processes that involve many cooperating functions [Brod98]. To build Cooperative Information Systems (CISs), existing ISs will have to be wrapped and adapted to support cooperation and interoperability. SoRware engineers expect to leverage distributed object technology and other middleware as well as reverse engineering and reengineering technology to achieve this migration to CISs effectively. However, many obstacles and challenges lie ahead.Firstly, there is no clear vision (let alone a standard) on what infrastructure to build future CISs. There is intense competition among software companies such as OMG (CORBA), IBM (NCF), Microsoft (Active), and Sun (JavaBeans) to roll out the dominant infrastructure for network-centric applications. Consequently, we seem to be years away Tom effective and stable infrastructure technology for CISs and yet we are trying to build CISs today. Instead of concentrating on the interoperability among information systems, we are forced to build infrastructure interoperability bridges.Secondly, the reverse engineering community is concentrating on the automatic extraction of myriad software artifact horn source code instead of concentrating on the harder problem of identifying business rules. Thirdly, the software reengineering camps are busy migrating imperative code to object-oriented platforms (e.g., C++ or Java) instead of migrating to cooperative agents.
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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.029 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".