Business process point analysis: survey experiments
Bibliographic record
Abstract
Purpose The purpose of this paper is to present business process point analysis (BPPA), a technique to measure business functional process size, based on function point analysis (FPA), and using business process model and notation (BPMN). This paper also discusses the assessment results of BPPA compared with FPA. Design/methodology/approach Two experimental studies with participants from academia and industry were conducted. The following aspects in the experimental studies were focused: similarity, application easiness, feasibility, and application benefits. The purpose of the experiment was to assess BPPA comparing with FPA as the BPPA design followed the FPA pattern. Findings Experimental results showed that both academia and industry groups highly rated similarity and application benefits for BPPA compared with FPA. However, only participants from industry highly rated BPPA for application easiness and feasibility. The results also showed that participants’ previous experiences did not influence their ratings on BPPA. Originality/value BPPA helps project managers to measure functional process size of business process management projects. As BPPA is derived from FPA, its mechanism is easily recognizable by project managers who are used to FPA. These results also show that both techniques are in most cases considered rather similar.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".