Source code modularization using lattice of concept slices
Bibliographic record
Abstract
Most legacy systems have been altered due to prolonged maintenance to the point that they deviate significantly from their original and intended design and consequently, they lack modularity. Static source code analysis techniques like concept assignment, formal concept analysis and program slicing, have been successfully used by researchers for program understanding and for restoring system design properties. In our approach we combine these three techniques, aiming to gain on their individual strengths and overcoming their weaknesses. Here we present a program representation formalism that we call the lattice of concept slices and a program modularization technique that aims to separate statements in a code fragment according to the concept they implement or they may belong to. The lattice shows the relationship between the statements of a program and the domain concepts that might be implemented by the statements. Using the lattice as a primary data structure we present two algorithms for decomposing the program into appropriate modules. The goal is to achieve a modularization such that the modules are self-contained, side effect free and the code duplication among nodes is minimal. The modularization process is illustrated with an example C program.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".