Dynamic Analysis of Software Systems using Execution Pattern Mining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Software system analysis for extracting system functionality remains as a major problem in the reverse engineering literature and the early approaches mainly rely on static properties of software. In this paper, we propose a novel technique for dynamic analysis of software systems to identify the implementation of the software features that are specified through a number of feature-specific task scenarios. The execution of task scenarios and application of data mining algorithm sequential pattern discovery on the generated traces allow us to extract common functionality associated with the corresponding feature-specific task scenarios. The extracted patterns are used to identify the groups of core functions that implement software features. The proposed approach can be used for program comprehension and feature to source code assignment. A case study on the Unix Xfig drawing tool has been provided
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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 | 0.001 | 0.002 |
| 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 it