Software Process Improvement Adoption and Benefits in Canadian and English‐Speaking Caribbean Software Development Firms
Bibliographic record
Abstract
Abstract The free‐trade agreements between developed and developing countries have created more opportunities for firms to sell high quality software products in the global market. Unfortunately, for decades the information systems (IS) community has been plagued with the delivery of low quality software products. It is widely accepted that software development firms need to adopt software process improvement (SPI) initiatives in an effort to produce these high quality software products. This outcome can increase the competiveness of such firms, which by extension can increase the likelihood of winning global contracts. However, the uptake of these SPI initiatives in developing countries is low vis‐à‐vis developed countries. In addition, most studies on SPI are conducted in developed countries, with many being case studies, and a few exploring its application in a developed versus developing environment. This study seeks to compare the awareness, adoption and benefits of SPI programs in Canadian and English‐speaking Caribbean (ESC) software development firms. It was found that the awareness and adoption of SPI are higher in Canadian firms in comparison to the ESC, while the main benefit of SPI adoption in both environments was improved software product quality.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".