Design of a software quality decision system: a computational intelligence approach
Bibliographic record
Abstract
This paper introduces an approximate reasoning system for assessing software quality and introduces the application of two computational intelligence methods in designing a software quality decision system, namely, granulation from fuzzy sets and rule-derivation from rough sets. This research is part of a computational intelligent systems approach to software quality evaluation, which includes a fuzzy-neural software quality factor-criteria selection model with learning and a rough-fuzzy-neural software quality decision system. Overall, computational intelligence results from a synergy of various combinations of genetic, fuzzy, rough and neural computing in designing engineering systems. Based on observations concerning software quality and the granulations of measurements in an extended form of the McCall software quality measurement framework, an approach to deriving rules about software quality is given. Quality decision rules express relationships between evaluations of software quality criteria measurements. A quality decision table is constructed relative to the degree of membership of each software quality measurement in particular granules. Decision-tables themselves are as a collection of sensors, which "sense" inputs and output conditions for rules. Rosetta is used to generate quality decision rules. The approach described in this paper illustrates the combined application of fuzzy sets and rough sets in developing a software quality decision system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".