Implicit context: easing software evolution and reuse
Bibliographic record
Abstract
Software systems should consist of simple, conceptually clean software components interacting along narrow, well-defined paths. All too often, this is not reality: complex components end up interacting for reasons unrelated to the functionality they provide. We refer to knowledge within a component that is not conceptually required for the individual behaviour of that component as extraneous embedded knowledge (EEK). EEK creeps into a system in many forms, including dependences upon particular names and the passing of extraneous parameters. This paper proposes the use of implicit context as a means for reducing EEK in systems by combining a mechanism to reflect upon what has happened in a system, through queries on the call history, with a mechanism for altering calls to and from a component. We demonstrate the benefits of implicit context by describing its use to reduce EEK in the Java TM Swing library. Categories and Subject Descriptors D.1 [Software]: Programming Techniques; D.2....
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".