Fuzzy Rules for Risk Assessment and Contingency Estimation within COCOMO Software Project Planning Model
Bibliographic record
Abstract
Software development can be considered to be the most uncertain project when compared to other projects due to uncertainty in the customer requirements, the complexity of the process, and the intangible nature of the product. In order to increase the chance of success in managing a software project, the project manager(s) must invest more time and effort in the project planning phase, which involves such primary and integrated activities as effort estimation and risk management, because the accuracy of the effort estimation is highly dependent on the size and number of project risks in a particular software project. However, as is common practice, these two activities are often disconnected from each other and project managers have come to consider such steps to be unreliable due to their lack of accuracy. This chapter introduces the Fuzzy-ExCOM Model, which is used for software project planning and is based on fuzzy technique. It has the capability to not only integrate the effort estimation and risk assessment activities but also to provide information about the estimated effort, the project risks, and the effort contingency allowance necessary to accommodate the identified risk. A validation of this model using the project’s research data shows that this new approach is capable of improving the existing COCOMO estimation 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".