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Record W2145301739 · doi:10.1109/wicsa.2005.5

A Case Study in Incremental Architecture-Based Re-engineering of a Legacy Application

2006· article· en· W2145301739 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceJavaSoftware engineeringSoftware architectureArchitectureProgramming languageArchitecture description languageArchitectural patternLegacy systemProcess (computing)Component (thermodynamics)Reference architectureSoftware developmentSoftwareSoftware construction

Abstract

fetched live from OpenAlex

Without rigorous software development and maintenance, software tends to lose its original architectural structure and become more difficult to understand and modify. ArchJava, a recently proposed implementation language which embeds a component-and-connector architectural specification within Java implementation code, offers the promise of preventing the loss of architectural structure. We describe a case study in which we incrementally re-engineer an existing implementation with an eroded architecture to obtain an ArchJava implementation that more closely matches an idealized architecture. Building on results from similar case studies, we chose an application consisting of over 16,000 source lines of Java code and 80 classes that exhibited many characteristics of real-world legacy applications. We describe our process, some lessons learned, as well as some perceived limitations with the tools, techniques and languages we used.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.201
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.284
Teacher spread0.257 · 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