Bibliographic record
Abstract
In software engineering, separating problem and solutionlevel concerns and analyzing each of them in an abstract manner are established principles (Ghezzi, Jazayeri, & Mandrioli, 2003). A software representation, for instance, a model or a specification, is a product of such an analysis. These software representations can vary across a formality spectrum: informal (natural language), semi-formal (mathematics- based syntax), or formal (mathematics-based syntax and semantics). As software representations become pervasive in software process environments, the issue of their communicative efficacy arises. Our interest here is in software representations that make use of natural language and their communicability to their stakeholders in doing so. In this article, we take the position that if one cannot communicate well in a natural language, then one cannot communicate via other, more formal, means. The rest of the article is organized as follows. We first outline the background necessary for later discussion. This is followed by the proposal for a framework for communicability software representations that are created early in the software process and the role of natural language in them. We then illustrate that in software representations expressed in certain specific nonnatural languages. Next, challenges and directions for future research are outlined and, finally, concluding remarks are given.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".