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Record W2108761763 · doi:10.1109/ease.2009.11

Change Support in Adaptive Software: A Case Study for Fine-Grained Adaptation

2009· article· en· W2108761763 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 Waterloo
Fundersnot available
KeywordsAdaptation (eye)Computer scienceGranularityHierarchyContext (archaeology)Software evolutionSet (abstract data type)Software engineeringSoftware systemSoftwareAdaptive systemSoftware constructionArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

Adaptive software is a closed-loop system which aims at adjusting itself in different situations at runtime. This paper looks at adaptation as changes in the context of dynamic software evolution, and proposes a conceptual model for these changes based on Activity Theory. This model consists of a hierarchy of activities making changes, and the objectives motivating these changes. This model is an attempt towards establishing a formal framework for designing adaptive software systems. While the proposed model is applicable to any type of adaptation, at different levels of granularity of various software systems, the paper focuses only on fine-grained adaptation changes. As a case study, a mission-critical e-commerce system, TPC-W, isused to apply the proposed model and evaluate the effectiveness of fine-grained adaptation changes. The conducted set of experiments aims at evaluating self-optimizing and self-configuring adaptation activities performed through several fine-grained actions such as service-level upgrading/degrading.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.923
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.164
GPT teacher head0.351
Teacher spread0.187 · 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