Representation of knowledge and inference rules in SEMEST+
Bibliographic record
Abstract
SEMEST+ is an extended version of the software engineering measurement expert system tool (SEMEST), which provides a rule-based software engineering measurement and analysis system on the Internet. The core part of SEMEST+ is the measurement knowledge base. Therefore, how to represent the knowledge of experts is the central issue in designing the system. In classical rule base systems, a rule may be specified using some special language, such as Prolog, with a built-in backward chaining inference engine for implementing an expert system. However, it is impossible for SEMEST+ to use Prolog for implementing a complicated Web-based application. Therefore, we should adopt a modern language to represent the inference rules and at the same time utilize the advantage of a generic database system to maintain the knowledge. Since XML has become the standard platform for structured data exchange especially on Web applications, the knowledge rules of SEMEST+ are represented in XML. The SEMEST+ inference engine is implemented in Java. Based on both measurement classical theories and industrial experience, SEMEST+ is implemented as a multiple-layered Web-based system supported by an expert inference engine and a knowledge base. SEMEST+ provides five categories of expert support, known as the goal-, process-, category-, application-domain- and organization-role-oriented measurement analyses, for the software industry to practice quantitative software engineering.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".