Improving software quality using Six Sigma DMAIC-based approach: a case study
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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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".