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Record W2616935805

Proceedings of the 2006 international workshop on Self-adaptation and self-managing systems

2006· article· en· W2616935805 on OpenAlexaffabout
Betty H. C. Cheng, Rogério de Lemos, Stephen Fickas, David Garlan, Jeff Magee, Hausi Müller, Richard Taylor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceAdaptation (eye)Flexibility (engineering)Software engineeringAutonomic computingNotationSoftware systemFault toleranceSystem of systemsSoftwareDistributed computingSystems design
DOInot available

Abstract

fetched live from OpenAlex

An increasingly important requirement for a software-based system is the ability to self-manage by adapting itself at run time to handle such things as changing user needs, system intrusions or faults, a changing operational environment, and resource variability. Such a system must configure and reconfigure itself, augment its functionality, continually optimize itself, protect itself, and recover itself, while keeping its complexity hidden from the user.The topic of self-adaptive and self-managing systems has been studied by various communities, including software architectures, fault-tolerant computing, robotics, control systems, programming languages, and biologically-inspired computing. The goal of this workshop is to bring together researchers and practitioners from many of these diverse areas to discuss the fundamental principles, state of the art, and critical challenges of self-adaptive systems. Specifically, we intend to focus on the software engineering aspects, including the methods, architectures, algorithms, techniques, and tools that can be used to support dynamic adaptive behavior.Self-adaptation in self-managing systems represents a major new concern for software engineering. While in the past methods, tools, and notations have focused on the problem of preventing defects from occurring in our fielded systems, increasingly this is not enough. In addition, systems must take a much more aggressive role in handling and adapting to run time problems. A central concern then becomes the engineering mechanisms that can support self-adaptation. Too often today's systems achieve run time flexibility only by hard wiring in special-purpose, low-level code (like exceptions and time outs) that is difficult to change, reuse, or analyze.The ICSE 2006 SEAMS workshop is a continuation of a number of successful workshops in the area of self-managing systems held at ICSE and FSE in recent years, including the FSE 2002 and 2004 Workshops on Self-Healing (Self-Managed) Systems (WOSS), ICSE 2005 Workshop on Design and Evolution of Autonomic Application Software (DEAS), and the ICSE 2002, 2003, 2004 and 2005 Workshops on Architecting Dependable Systems (WADS). The objective is to consolidate the interest in the software engineering community on autonomic, self-managing, self-healing, self-optimizing, self-configuring, and self-adaptive systems through this new integrated workshop. This will be the first of several workshops to assess progress and identify challenges in this important area.We have received 22 submissions from academic and industrial contributors. Each paper was reviewed by at least 3 members of the Program Committee, and a total of 13 full papers have been accepted for presentation. We are thankful for the support and dedication of the Program Committee members towards making this workshop a success. The Program Committee consisted of: Gordon Blair (University of Lancaster, UK), Cristina Gacek (University of Newcastle upon Tyne, UK) Mike Hinchey (NASA Goddard, USA), Marin Litoiu (IBM Toronto, Canada), Neno Medvidovic (University of Southern California, USA), John Mylopoulos (University of Toronto, Canada), Masoud Sadjadi (Florida International University, USA), Dennis Smith (SEI, USA), Roy Sterritt (University of Ulster, UK), Alexander Wolf (University of Lugano, Switzerland), Kenny Wong (University of Alberta, Canada).

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0060.006
Open science0.0030.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0420.013

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 head0.020
GPT teacher head0.242
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations7
Published2006
Admission routes2
Has abstractyes

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