An Approach for Detecting Execution Phases of a System for the Purpose of Program Comprehension
Bibliographic record
Abstract
Understanding the behavioural aspects of a software system is an important activity in many software engineering activities including program comprehension and reverse engineering. The behaviour of software is typically represented in the form of execution traces. Traces, however, tend to be considerably large which makes analyzing their content a complex task. There is a need for trace simplification techniques that can help software engineers make sense of the content of a trace despite the trace being massive. In this paper, we present a novel algorithm that aims to simplify the analysis of a large trace by detecting the execution phases that compose it. An example of a phase could be an initialization phase, a specific computation, etc. Our algorithm processes a trace generated from running the program under study and divides it into phases that can be later used by software engineers to understand where and why a particular computation appears. We also show the effectiveness of our approach through a case study.
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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".