Decompiling Java using staged encapsulation
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 paper presents an approach to program structuring for use in decompiling Java bytecode to Java source. The structuring approach uses three intermediate representations: (1) a list of typed, aggregated statements with an associated exception table, (2) a control flow graph, and (3) a structure encapsulation tree. The approach works in six distinct stages, with each stage focusing on a specific family of Java constructs, and each stage contributing more detail to the structure encapsulation tree. After completion of all stages the structure encapsulation tree contains enough information to allow a simple extraction of a structured Java program. The approach targets general Java bytecode including bytecode that may be the result of front-ends for languages other than Java, and also bytecode that has been produced by a bytecode optimizer. Thus, the techniques have been designed to work for bytecode that may not exhibit the typical structured patterns of bytecode produced by a standard Java compiler. The structuring techniques have been implemented as part of the Dava decompiler which has been built using the Soot framework.
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 | 0.000 | 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 | 0.000 | 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