An extension of SEMEST: the online software engineering measurement tool
Bibliographic record
Abstract
Software engineering measurement and metrics are key technologies toward quantitative software engineering. Current software measurement tools are application specific and usually cover only one or a few measures/metrics in software engineering. To address this problem, a comprehensive software engineering measurement expert system tool (SEMEST) was developed. In order to enhance the current version of SEMEST, SEMEST+ is designed by extending the power and performance of its earlier version. SEMEST+ is a Web-based software measurement expert tool to provide a comprehensive set of software measures and metrics in a rigorous way. The extensions of SEMEST are focused on updating the knowledge base and its performance. First, measurements for different application domains and roles of software engineering are added to the knowledge base. This extension enables SEMEST+ to support five domains of software engineering measurement, i.e., goal-, process-, category-, domains-, and roles-oriented measurement, and their analysis. Second, we adopt data mining technologies to analyze large sets of measurement data collected from the software industry. For example, data on defects and productivity may be analyzed and benchmarked by using the tool. Third, recommendations can be derived based on the measurement results in order to improve an organization's practice and the quality of work products.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".