A customizable pattern‐based software process simulation model: design, calibration and application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Software process analysis and improvement relies heavily on empirical research. Empirical research requires measurement, experimentation, and modeling. However, whatever evidence is gained via empirical research is strongly context dependent. Thus, it is hard to combine results and capitalize upon them for the purpose of improvement in evolving development environments. The process simulation model GENSIM 2.0 addresses these challenges. GENSIM 2.0 is a generic process simulation tool representing V‐model type software development processes. Compared to existing process simulation models in the literature, the novelty of GENSIM 2.0 is twofold. Firstly, its model structure is customizable to organization‐specific processes. This is achieved by using a limited set of generic structures (macro‐patterns). Secondly, its model parameters can be easily calibrated to available empirical data and expert knowledge. This is achieved by making the internal model structures explicit and by providing guidance on how to calibrate model parameters. This article outlines the structure of GENSIM 2.0, gives examples on how to calibrate the model to available empirical data, and demonstrates its usefulness through two application scenarios The first scenario illustrates how GENSIM 2.0 helps in finding effective combinations of verification and validation techniques under given time and effort constraints. The second scenario shows how the simulator supports in finding the best combination of alternative verification techniques. Copyright © 2009 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it