INSTEP: A static instrumentation framework for preserving extra-functional properties
Bibliographic record
Abstract
Tracing is a well-established method for debugging programs. Current approaches aim only at preserving functional correctness during the instrumentation. Preservation of functional correctness is a necessary feature of all instrumentation tools. However, few existing instrumentation tools preserve extra-functional properties of a program. Specific classes of software are unable to leverage software instrumentation; e.g., timing for real-time systems, memory consumption for embedded software, and tracing bandwidth for on-board software. We present the first instrumentation framework, INSTEP, that preserves logical correctness and a rich set of extra-functional properties. INSTEP derives instrumentation alternatives based on the developer's instrumentation intent (II), abstracts the program and prunes the search space, and then instruments the program based on constraints and cost models of competing properties. We demonstrate and experiment with a fully automated framework of INSTEP with different IIs and extra-functional properties.We also experiment with a large automotive case study to show the scalability of INSTEP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".