TIDIER: an identifier splitting approach using speech recognition techniques
Bibliographic record
Abstract
SUMMARY The software engineering literature reports empirical evidence on the relation between various characteristics of a software system and its quality. Among other factors, recent studies have shown that a proper choice of identifiers influences understandability and maintainability. Indeed, identifiers are developers' main source of information and guide their cognitive processes during program comprehension when high‐level documentation is scarce or outdated and when source code is not sufficiently commented. This paper proposes a novel approach to recognize words composing source code identifiers. The approach is based on an adaptation of Dynamic Time Warping used to recognize words in continuous speech. The approach overcomes the limitations of existing identifier‐splitting approaches when naming conventions (e.g., Camel Case) are not used or when identifiers contain abbreviations. We apply the approach on a sample of more than 1000 identifiers extracted from 340 C programs and compare its results with a simple Camel Case splitter and with an implementation of an alternative identifier splitting approach, Samurai. Results indicate the capability of the novel approach: (i) to outperform the alternative ones, when using a dictionary augmented with domain knowledge or a contextual dictionary and (ii) to expand 48% of a set of selected abbreviations into dictionary words. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".