Application of Execution Pattern Mining and Concept Lattice Analysis on Software Structure Evaluation.
Bibliographic record
Abstract
Software maintenance activities for producing a feature-rich system tend to impair the software’s structure into an unshaped and cost-prone legacy system. Thus, it is desir-able to keep track and measure the impacts of the newly added features on the structure of the software system. The proposed technique in this paper is based on extracting fre-quent patterns in the execution traces of a software system using a pattern discovery technique. The patterns represent functionalities that correspond to the feature specific sce-narios. In a further step, the generated execution patterns are distributed on a concept lattice to separate feature spe-cific patterns from commonly used patterns. The proposed technique allows for assigning software features onto the software system modules and provides a means for assess-ing the degree of functionality scattering among the system modules. Consequently, we measure the impact of individ-ual features on the structure of the system. A case study on the Unix Xfig drawing tool is used to present the accuracy of the approach.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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".