Discovering Reusable Functional Features in Legacy Object-Oriented Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Typical object-oriented (OO) systems implement several functional features that are interwoven into class hierarchies. In the absence of aspect-oriented techniques to develop and compose these features, developers resort to object-oriented design and programming idioms to separate features as well as possible. Given a legacy OO system, discovering existing functional features helps understand the design of the system and extract these features to ease their maintenance and reuse. We want to discover candidate functional features in OO systems. We first define functional features and then discuss the footprints that such features are likely to leave in an OO system. We identify three such footprints: (1) multiple inheritance, (2) delegation, and (3) ad-hoc. We develop a set of algorithms for identifying such footprints in OO code and implemented them for the Java language using Eclipse JDT. In this article, we present the algorithms, and the results of applying the corresponding tools on five open-source systems: FreeMind, JavaWebMail, JHotDraw, JReversePro, and Lucene. Our experimental results show that: (1) the different algorithms can identify interesting and useful candidate functional features in OO systems, (2) they can identify opportunities for refactoring, and (3) they are complementary and could help developers.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it