Utilizing Usability Evaluating Model in Applying CMM to Improve the Quality of Software Maintenance Process
Bibliographic record
Abstract
Maintenance plays an important role in the life cycle of a software product. It is estimated that there are more than 100 billion lines of code in production in the world. As much as 80% of it is unstructured, patched and not well documented. Maintenance can alleviate these problems. The purpose of this paper is to explore the use of the Capability Maturity Model (CMM) to improve the quality of software maintenance process (SMP). The architecture of the CMM model has been retained almost as is while its content, which was specific to the development process, has been either modified or extended to take into account the characteristics specified to the maintenance function, these characteristics were then organized into key process areas (KPAs) of CMM model. This paper applied the definition of (ISO 9241-11, 1998) that examines effectiveness, efficiency, and satisfaction. The emphasis will be given to the SMP activities
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".