Improving software quality using Six Sigma DMAIC-based approach: a case study
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
Purpose Managing quality is a vital aspect in software development world, especially in the current business competition for the fast delivery of feature rich products with high quality. For an organization to meet its intended level of excellence in order to ensure its success, a culture of quality should be built where every individual is responsible of quality and not just the software testing team. However, delivering software products with very few bugs is a challenging constraint that is usually sacrificed in order for a company to meet other management constraints such as cost, scope and scheduling. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors present a Six Sigma DMAIC-based framework for improving software quality. Different phases of DMAIC methodology are applied for quality improvement in one of the largest software applications for “RK” company (name anonymized) in Canada where critical to quality aspects are identified, production bugs classified and measured, the causes of the large number of production bugs were specified leading to different improvement suggestions. Several metrics were proposed to help “RK” company control its software development process to ensure the success of the project under study. Findings This paper shows how companies can use a systematic approach such as DMAIC to eliminate errors and improve efficiency. It helps them to identify and implement improvements that leads to an increased confidence in the quality of the product produced at all levels. Originality/value By applying DMAIC at “RK” company the authors were able to demonstrate how DMAIC can help organizations improve the quality of their software products. As a result, reduce cost and cycle times, achieve customer satisfaction and improve profit margin.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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