Identification et localisation des preoccupations fonctionnelles dans du code legataire java
Bibliographic record
Abstract
Object oriented applications integrate various functional aspects. These aspects can be scattered everywhere in the code. There are various types of aspects: • aspects which represent business functionalities; • aspects related to non functional requirements or to design concerns such as robustness, distribution, and security. The code representing such aspects can be located in different class hierarchies. Researchers have been interested in the problem of the modularisation of these aspects and many approaches were proposed: oriented programming subjects, oriented programming Aspects and oriented programming view. These approaches offer techniques and tools for designing object oriented applications based on the composition of slices of various aspects. The main benefit of the separation of aspects is supporting reuse and maintenance. Consequently, it is well worth identifying and extracting aspects of legacy object oriented applications. Our work mainly focuses on functional aspects. Assuming that the code of a functional aspect or a feature has a functional cohesion (dependencies between elements), we suggest methods for identifying such features from the code. The idea is to identify, in the absence of any aspect oriented paradigm, the techniques used for implementing a feature in the code. Our approach consists of: identifying techniques used by developers to integrate a feature in the absence of aspect oriented techniques; • characterizing the patterns of these techniques; • and developing tools to identify these patterns. We present two approaches for the identification of the existing features in the object oriented code. The first one identifies various design patterns which integrates these features in the code. The second approach uses the formal concept analysis to identify the recurring features in the code. We experiment our approaches to identify functional features in different open source object oriented applications. The results show the efficiency of our approaches in identifying various functional features in the legacy object oriented, and can some times suggest refactoring. Keywords: Features, reengineerings, legacy object oriented code, refactoring, Galois lattices, Java.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".