A customizable pattern‐based software process simulation model: design, calibration and application
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".