Mismatch handling for COTS selection: a case study
Bibliographic record
Abstract
Abstract Using Commercial Off‐The‐Shelf (COTS) products to build software systems requires the evaluation of existing COTS products, the selection of the COTS that best fit system requirements, and the integration of the selected COTS into the system. During this process, it is inevitable to encounter two types of mismatches: (1) COTS mismatches, which are encountered during COTS selection between requirements and COTS features, due to an excess or shortage of COTS features, and (2) architectural mismatches, which arise when integrating multiple COTS products that do not fit well together. This paper focuses on the ‘COTS mismatch’ problem. The paper describes a real‐world case study of a single COTS selection with the aid of a method called MiHOS (Mismatch Handling for COTS Selection) to handle the COTS mismatches. MiHOS runs on top of existing COTS selection models in order to properly address COTS mismatches with limited resources. The case study was conducted in the e‐service domain. The goal was to validate MiHOS and to answer two key questions: (1) How useful is decision support during different phases in the process mismatch handling? And (2) what is the effort vs benefit of using MiHOS? The results are promising and illustrate the potential benefits of using MiHOS. Copyright © 2011 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".