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Record W2252116325

Reverse engineering of object-oriented code into Umple using an incremental and rule-based approach

2014· article· en· W2252116325 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputer Science and Software Engineering · 2014
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceProgramming languageUnified Modeling LanguageJavaReverse engineeringCode (set theory)Object-oriented programmingCode generationSource codeMetamodelingKPI-driven code analysisSoftware engineeringTheoretical computer scienceStatic program analysisSoftware developmentSoftwareOperating system
DOInot available

Abstract

fetched live from OpenAlex

In this paper we present a novel approach to reverse engineering, in which modeling information such as UML associations, state machines and attributes is incrementally added to code written in Java or C++, while maintaining the system in a textual format. Umple is a textual representation that blends modeling in UML with programming language code. The approach, called umplification, produces a program with behavior identical to the original one, but written in Umple and enhanced with model-level abstractions. As the resulting program is Umple code, our approach eliminates the distinction between code and model. In this paper we discuss the principles of Umple, the umplification approach and a rule-driven tool called the Umplificator, which implements and validates the depicted approach.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.106
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.239
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it