A Hybrid eBusiness Software Metrics Framework for Decision Making in Cloud Computing Environment
Bibliographic record
Abstract
Developing high-quality software is essential for eBusiness organizations to cope with drastic market competition. With the development of cloud computing technologies, eBusiness systems and applications pay more attention to open endedness. In a cloud computing environment, eBusiness systems have the ability to provide information technology resources on demand. Traditional software metric methods in distributed systems and applications are technical and project driven, making the market demand and internal practical operation not perfectly balanced within a cloud-computing-based eBusiness corporation. To address this issue, this paper presents a hybrid framework based on the goal/question/metric paradigm to evaluate the quality and efficiency of previous software products, projects, and development organizations in a cloud computing environment. In our approach, to support decision making at the project and organization levels, three angular metrics are used, i.e., project metrics, product metrics, and organization metrics. Furthermore, an improved radial-basis-function-based model is also provided to manage existing projects and design new projects. Experimental results on a well-known eBusiness organization show that the proposed framework is effective, efficient, and operational. Moreover, using the described decision-making algorithm, the predicted data are very close to actual results on the software cost, the fault rate, the development workload, etc., which are greatly helpful in achieving high-quality software.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".