Using Tabular Expression Input to Specify and Generate Program Family Members Using Tabular Expression Input to Specify and Generate Program Family Members
Bibliographic record
Abstract
When one is to develop program families traditional programming methods which are intended for the development of a single program are not appropriate. There are several different approaches available. For example the Draco approach in [18] and the product line engineering process described in [23]. Here we will focus on the FAST process developed at Lucent [24] because it is a reasonably systematic process for engineering families that has been used at Lucent Technologies for years and has proven successful. Lucent/Bell Labs have had success in reducing programming costs and speeding up development times by using the FAST process. In the FAST process, after identifying the commonalities and parameters of variation in family of programs, a special purpose programming language is developed. This language is suitable for writing members of the family. The common features of the family are already built-in; only the differences need to be programmed. McMaster University's Software Engineering Research Group (Hamilton, Ontario, Canada) is developing a set of tools to support the use of a broad class of tabular expressions (TTS). Using tabular expressions one may present complex conditional expression in a way that is more easily read, analyzed, and more likely to be correct than either conventional mathematics or conventional programs. In this work I propose to combine these two technologies. I will enhance the FAST process using tabular notation and table-based tools. The results of the commonality analysis of the FAST process, as developed at Lucent, will be used to develop a set of incomplete tabular expressions. The completed parts will represent the commonalities. The incomplete parts will correspond to the parameters of variation. The application engineer must complet...
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".