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Record W2527012254 · doi:10.1145/2934466.2934469

A mathematical model of performance-relevant feature interactions

2016· article· en· W2527012254 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
TopicSoftware System Performance and Reliability
Canadian institutionsUniversity of Waterloo
FundersSpecialized Research Fund for the Doctoral Program of Higher Education of ChinaNational Natural Science Foundation of China
KeywordsComputer scienceFeature (linguistics)Task (project management)SoftwareTheoretical computer scienceSoftware systemMachine learningArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

Modern software systems have grown significantly in their size and complexity, therefore understanding how software systems behave when there are many configuration options, also called features, is no longer a trivial task. This is primarily due to the potentially complex interactions among the features. In this paper, we propose a novel mathematical model for performance-relevant, or quantitative in general, feature interactions, based on the theory of Boolean functions. Moreover, we provide two algorithms for detecting all such interactions with little measurement effort and potentially guaranteed accuracy and confidence level. Empirical results on real-world configurable systems demonstrated the feasibility and effectiveness of our 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.224

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 communication0.0000.001
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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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