TRIS: A Fast and Accurate Identifiers Splitting and Expansion Algorithm
Bibliographic record
Abstract
Understanding source code identifiers, by identifying words composing them, is a necessary step for many program comprehension, reverse engineering, or redocumentation tasks. To this aim, researchers have proposed several identifier splitting and expansion approaches such as Samurai, TIDIER and more recently GenTest. The ultimate goal of such approaches is to help disambiguating conceptual information encoded in compound (or abbreviated) identifiers. This paper presents TRIS, TRee-based Identifier Splitter, a two-phases approach to split and expand program identifiers. First, TRIS pre-compiles transformed dictionary words into a tree representation, associating a cost to each transformation. In a second phase, it maps the identifier splitting/expansion problem into a minimization problem, i.e., the search of the shortest path (optimal split/expansion) in a weighted graph. We apply TRIS to a sample of 974 identifiers extracted from JHotDraw, 3,085 from Lynx, and to a sample of 489 identifiers extracted from 340 C programs. Also, we compare TRIS with GenTest on a set of 2,663 mixed Java, C and C++ identifiers. We report evidence that TRIS split (and expansion) is more accurate than state-of-the-art approaches and that it is also efficient in terms of computation time.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".