Organization Global Software Development Challenges of Software Product Quality
Bibliographic record
Abstract
Leaders of global software development (GSD) processes in organizations have been confronting low software product quality. Managers of these processes have faced challenges that have been affecting customer satisfaction and that have resulted in negative social impacts on public safety, business financial performance, and global economic stability. The purpose of this qualitative exploratory multiple case study was to discover a common understanding shared by managers in Canadian GSD organizations of how to meet software product quality goals and enhance customer satisfaction. The conceptual framework for the study was based on Deming's 14 principles of quality management. The purposeful sample included 30 knowledgeable participants who worked in Canada as GSD managers. Semistructured interviews conducted through telephone and audioconference tools, along with the review of related documents, were used to gather data. Eight themes emerged from the data analysis: developing a clear purpose and work principles, improving processes and employee skills, developing adequate personnel management strategies, promoting autonomy and personal worker development, formulating life cycle and development techniques, identifying challenges, formulating solutions, and focusing on product quality. The research findings have implications for positively influencing social change through the provision of methods and process knowledge to GSD organizational leaders. This information consists of best management and industry practices that can be applied to achieve software product quality and customer satisfaction, create management systems, maintain a competitive advantage, and prevent global software development project failures.
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.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".