IMMoS: a methodology for integrated measurement, modelling and simulation
Bibliographic record
Abstract
Abstract One reason for the relatively small number of real‐world applications of simulation in software engineering is the existing lack of guidance in creating and formulating the related models and in the isolated usage of associated techniques. In order to support both strategic and project management in software organizations, a methodology for integrated measurement, modelling and simulation (IMMoS) was developed and validated. The hybrid approach integrates the individual strengths of its inherent methodological elements and concepts. The core element of IMMoS is the simulation modelling method system dynamics (SD), which integrates quantitative dynamic models with quantitative and qualitative static models in a natural way. The novelty of IMMoS is twofold. First, it enhances existing guidance for SD modelling by adding a component that enforces goal‐orientation, and by providing a refined process model with detailed description of activities, entry/exit criteria, input/output products, and roles involved. Secondly, it describes how to combine SD modelling with goal‐oriented measurement and descriptive process modelling, thus improving efficiency and smoothly closing the gap to established methods in empirical software engineering. IMMoS has been initially evaluated. The effectiveness and efficiency of IMMoS is supported with empirical evidence from two industrial case studies and one controlled experiment. Copyright © 2003 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".