Metrics for Measuring the Effectiveness of Decompilers and Obfuscators
Bibliographic record
Abstract
Java developers often use decompilers to aid reverse engineering and obfuscators to prevent it. Decompilers translate low-level class files to Java source and can produce "good" output. Obfuscators transform class files into semantically-equivalent versions that are either: (1) difficult to decompile, or (2) decompilable, but result in "hard- to-understand" Java source. We present a set of metrics developed to quantify the effectiveness of decompilers and obfuscators. The metrics include some selective size and counting metrics and an expression complexity metric. We have applied these metrics to evaluate a collection of decompilers and obfuscators. By quantitatively comparing original Java source against decompiled and obfuscated code respectively, we show which decompilers produce "good" code and whether obfuscations result in "hard-to-understand" code.
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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 | 0.004 | 0.001 |
| 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".