Exploratory study on an innovative use of COSMIC-FFP for early quality assessment
Bibliographic record
Abstract
The functional size measurement method, COSMIC-FFP, adopted as the ISO/IEC 19761 standard in 2003, was developed by the Common Software Measurement International Consortium (COSMIC). It focuses on the "user view" of functional requirements and is applicable throughout the development life cycle. As some of the software systems targeted by COSMIC-FFP are large-scale and inherently complex, feedback on their functional complexity would facilitate containment of that complexity throughout the software life cycle. In this thesis, a new early quality assessment of COSMIC-FFP models is proposed. The benefits of this work include earlier prediction of the functional complexity of the behavior of software in the COSMIC-FFP context, right from the requirements phase, as well as a mechanism for generating black-box test cases from the COSMIC-FFP model, test case prioritization and test set adequacy monitoring and optimization within given budget constraints, and an early prediction of reliability based on Markov chains. We also present a study of the scales, units and scale types of both COSMIC-FFP and the Entropy-based Functional Complexity Measure that forms the basis of the testing assessment method we propose here. Previous studies have analyzed the scale types of many pieces of software, but not the concept of scale itself, nor how it is used in the design of a measurement method. Two well-known case studies are introduced to demonstrate the applicability of the proposed methods: the Hotel Accommodation System and the Railroad System. We include a formalized COSMIC-FFP definition in the AS-TRM context (Autonomic Systems Timed Reactive Object Model), a language for the formal design of autonomic reactive systems developed at Concordia University. We introduce the Steam Boiler case study to demonstrate the applicability of formalizing COSMIC-FFP in the AS-TRM context. Future work based on this thesis can include the development of AS-TRM specifications for several benchmark case studies, and the collection of COSMIC-FFP measurement data for both the theoretical and empirical validation of the proposed measurement method. The testing method proposed here has been adapted to a specific class of projects, namely Enterprise Resource Planning (ERP) projects, which are perceived to be mission-critical initiatives in many organizations. They can be found in business transformation programs and are instrumental in improving organizational performance.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".