Towards providing decision support for COTS selection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The evolution of software engineering has led to component-based software development, which in turn has engendered 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 process of selecting COTS products is characterized by a multiplicity of challenges, which should be addressed in order to harness the benefits of COTS-based software development. In this paper we preset a model that splits the COTS selection process into layers; basing on the (intra-layer) activities which affect the choice of a decision support to address a particular challenge. Moreover, we evaluate the COTS selection methods in the reviewed literature according to how they address the challenges. Finally we present the functionalities of an ideal decision support system (DSS) for COTS selection, as well as the techniques for achieving the functionalities
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it