Static And Dynamic Reverse Engineering Techniques for Java Software Systems
Bibliographic record
Abstract
The main contributions of this dissertation are as follows: methods for using the dependencies between static and dynamic models for goal driven reverse engineering tasks, including merging dynamic information to a static Rigiview; using static information to guide the generation of dynamici nformation; slicing a Rigi view using SCED scenarios; and raising the level of abstraction of SCED scenarios using a high-level Rigigraph; algorithms for optimizing synthesized state diagrams using UMLnotation; application of the synthesis algorithm presented by Koskimies and Mäkinen [54] to SCED; string matching algorithms for raising the level of abstraction of SCED scenario iagrams; the prototype reverse ngineering environment Shimba, which integrates two existing tools: Rigi for reverse engineering the static structure of Javasoftware; and SCED and its state diagram synthesis facility for reverse engineering the dynamic behavior of Java software; methods and tools for gathering information, including extraction of static information from Java byte code;and extraction of run-time information by running the target system under a customized jdk debugger; a case study to evaluate the facilities of Shimba.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".