Optimized mismatch resolution for COTS selection
Bibliographic record
Abstract
Abstract The use of Commercial Off‐The‐Shelf (COTS) products in the software development process requires the evaluation of existing COTS products, and then selecting the one that best fits system requirements. In this process, it is inevitable to encounter mismatches between COTS features and system requirements. Mismatches occur as a result of an excess or shortage of COTS capabilities. Many of these mismatches are resolved after selecting a COTS product. Existing COTS‐selection approaches fail to properly consider these mismatches. This article presents MiHOS (Mismatch Handling for COTS Selection), an approach that aims at addressing mismatches while considering limited resources. MiHOS can be integrated with existing COTS‐selection methods at two points: (i)When evaluating COTS candidates in order to estimate the anticipated fitness of the candidates if their mismatches are resolved. This helps to base our COTS‐selection decisions on the fitness that the COTS candidates will eventually have if selected. (ii) After selecting a COTS product in order to plan the resolution of the most appropriate mismatches using suitable actions, such that the most important risk, technical, and resource constraints are met. A case study from the e‐services domain is used to illustrate the method and to discuss its added value. Copyright © 2008 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 | 0.003 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".