Bibliographic record
Abstract
Today many organizations are seeking software process improvement (SPI) to\nimprove their organizational capacity to deliver quality software. Last two decades\nhave seen a proliferation of SPI models and methodologies. But SEI statistics yet\nindicate the failure of the most of the companies to achieve their process improvement\ngoals. An analysis of SPI literature suggests that management commitment is most\nfrequently cited success factor in this regard. Thus making management commitment\nhappen in SPI has emerged as an essential factor in SPI success. Extant SPI literature\nhas explored some aspects of management commitment, nevertheless prime question\nis un-answered that how SPI practitioners cope with this challenge.\nAddressing this question we conducted this study that is based on qualitative\ninterviews and questionnaires with sixteen SPI practitioners in fourteen software\norganizations across Sweden, Pakistan, USA, France and Canada. It reports\nmotivators, de-motivators and indicators of management commitment. The findings of\nthis study can help SPI practitioners in designing SPI initiatives that will render\nenhancement in management commitment. Furthermore, this study implies that any\nSPI research conducted in a specific context can lead to inconsistent results due to\ncultural impacts.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".