An empirical evaluation of the ISO/IEC 15504 assessment model
Bibliographic record
Abstract
The emerging International Standard ISO/IEC 15504 (Software Process Assessment) includes an exemplar assessment model (known as Part 5). From data collected thus far, the majority of users of ISO/IEC 15504 employ the exemplar model as the basis for their assessments. This makes it important to perform systematic empirical evaluations of this model. Such evaluations would provide a substantiated basis for using the model, as well as give the developers of ISO/IEC 15504 information as to the necessary improvements to make. Questionnaire data was collected from the lead assessors of 57 assessments world-wide. Our findings indicate that a majority of the assessors used Part 5 as a source of indicators for conducting their assessments and they found Part 5 both useful and easy to use. Furthermore, they were satisfied with the level of detail of the exemplar model, although a minority indicated that less detail in the collected evidence would not have harmed the accuracy of their judgements. However, the assessors also expressed doubts about the consistency and repeatability of their process attribute ratings. A closer examination indicated a concern with ratings at levels 4 and 5. Finally, they found it easier to rate at the extremes of the rating scale, but clearly had more difficulty rating at the middle of the scale. These findings are encouraging in that they indicate that the current model can be used successfully in assessments. However, they also ,point out some weaknesses in the rating scheme that need to be rectified in future revisions of ISO/IEC 15504.
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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 | 0.308 | 0.482 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".