Bibliographic record
Abstract
In the last decade, the world of software development has evolved rapidly. This evolution has led to component-based software development, which in turn, has generated tremendous interest in the development of plug-and-play reusable software, leading to the concept of Commercial Off-The-Shelf (COTS) software components. The use of COTS is increasingly becoming commonplace. This is mainly due to shrinking budgets, accelerating rates of COTS enhancement, development time and effort constraints, and expanding system requirements. However, the COTS marketplace is characterized by a vast array of products and product claims, as well as extreme quality and capability differences among products. Therefore, there is need for a robust Decision Support System (DSS) to assist software developers and other stakeholders to select appropriate components for projects. In this research we propose an agent-based DSS for COTS selection and negotiation. The main task of the agents is to assist COTS selection stakeholders to conduct simulation-based evaluation of COTS products so as to determine the impact of different scenarios of COTS selection needs, preferences and constraints. Being web-based, the proposed DSS improves information sharing among stakeholder. Moreover, it addresses uncertainty issues related to credibility of information source, ability of the COTS selection team, changing system requirements, and changing COTS characteristics due to updates of COTS products.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".