Strengthening maturity levels by a legal assurance process
Bibliographic record
Abstract
Abstract One of the key elements for the viability of information system projects is given by the adoption of legal assurance activities and measures since nowadays they can arise legal risks that, in some cases, can suppose a serious threat for project commercial and financial success. When calculating the return of investment (ROI) for a software process improvement initiative, readers would not take care which are the cost issues impacting on such values, supposing the activities generating such value are referable only to the processes included in a Maturity Model (MM) such as CMMI or ISO 15504. During last years, moving from the initial Philip Crosby's idea for measuring and checking the organizational evolution of an organization, a plenty of MM have been created, but there is no news about a legal assurance (LAS) process that make more systematic the way legal risks are (or should be) managed. On the other hand, professional practice usually does not incorporate standardized processes in order to discipline the legal assurance activities and measures, returning a feeling for a lack of project legal security. This article proposes to take care of LAS process as an additional process area within an MM, in order to provide a suitable instrument for the management of inherent legal risks to any information systems project. After presenting main elements for this new process, it will be presented using the typical CMMI Process Area architecture, where it would be configurable as a support process at Maturity Level 2 (ML2). 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 | 0.034 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".