An empirical evaluation of static, dynamic, and hybrid slicing of WebAssembly binaries
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
The WebAssembly standard aims to form a portable compilation target, enabling the cross-platform distribution of programs written in a variety of languages. This paper introduces and evaluates novel slicing approaches for WebAssembly, including dynamic and hybrid approaches. Given a program and a location in that program, a program slice is a reduced program that preserves the behavior at the given location. A static slice does so for all possible inputs, while a dynamic slice does so for a fixed set of inputs. Hybrid slicing is a combination of static and dynamic slicing. We build on Observational-Based Slicing (ORBS), where we explore the design space for instantiating ORBS for WebAssembly. For example, ORBS can be applied to the whole program or to only the function containing the slicing criterion, and it can be applied before compilation to WebAssembly or afterwards. We evaluate the slices produced using various options quantitatively and qualitatively. Our evaluation reveals that dynamic slicing at the level of a function from a WebAssembly binary finds a sweet spot in terms of slice time and slice size, and that a combination of static and dynamic slicers achieves the best trade-off in terms of slicing time and slice size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.000 | 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