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Record W2097710325 · doi:10.1145/1082983.1083180

A design for evidence - based soft research

2005· article· en· W2097710325 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

VenueACM SIGSOFT Software Engineering Notes · 2005
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceSoftware engineeringProcess (computing)ArchitectureData scienceEmpirical evidenceTriangulationManagement scienceSystems engineeringEngineering

Abstract

fetched live from OpenAlex

Active research is being done in how to go from requirements to architecture. However, no studies have been attempted in this area despite a long history of empirical research in software engineering (SE). Our goal is to establish a framework for the transformation from requirements to architecture on the basis of a series of empirical studies. The first step is to collect evidence about practice in industry before designing relevant techniques, methods and tools. As part of this step, we use an interview-based multiple-case study with a carefully designed process of conducting the interviews and of preparing the data collected for analysis while preserving its integrity. In this paper, we describe the design of this multiple-case study, delineate the evidence trail, discuss validity issues, outline the data analysis focus, discuss meta issues on evidence-based SE particularly on combining and using evidence, describe triangulation approaches, and present two methods for accumulating evidence.

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)0.004
metaresearch head score (Gemma)0.658
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-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: Methods
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.658
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.001
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 head0.134
GPT teacher head0.349
Teacher spread0.215 · 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