Source-Level Linkage: Adding Semantic Information to C++ Fact-bases
Bibliographic record
Abstract
Facts extracted from source code have been used to support a variety of software engineering activities, ranging from architectural understanding, through detection of design patterns, to program exploration. Several fact extractors have been developed and published in the literature, but most of them extract facts only from individual compilation units. Linking multiple fact-bases is largely overlooked. Source-level linkage is different from compilation linkage. Its goal is to assist a software engineer, not to produce an executable program. Thus a source-level linker needs to collect as many as possible facts that may be potentially helpful to a software engineer's task, many of which are not available from a compiler linker. We present the design of a source-level linker for C++. This linker has been used to analyze a dozen of Microsoft Foundation Classes (MFC) programs and over 200 C++ programs that cover an extensive subset of C++ features, including templates from the standard template library (STL). As a further validation, we design a structural constraint language, SCL, to express and machine-check a wide range of constraints on the abstract semantics graph (ASG) produced by the linker
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".