Pattern matching for design concept localization
Bibliographic record
Abstract
The effective synergy of a number of different techniques is the key to the successful development of an efficient reverse engineering environment. Compiler technology, pattern matching techniques, visualization tools, and software repositories play an important role for the identification of procedural, data, and abstract-data-type related concepts in the source code. This paper describes a number of techniques used for the development of a distributed reverse engineering environments. Design recovery is investigated through code-to-code and abstract-descriptions-to-code pattern matching techniques used to locate code that may implement a particular plan or algorithm. The code-to-code matching uses dynamic programming techniques to locate similar code fragments and is targeted for large software systems (1MLOC). Patterns are specified either as source code or as a sequence of abstract statements written in an concept language developed for this purpose. Markov models are used to compute similarity measures between an abstract description and or code fragment in terms of the probability that a given abstract statement can generate a given code fragment. The abstract-description-to-code matcher is under implementation and early experiments show it is a promising technique.
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 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 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".