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Record W2519113869 · doi:10.1109/ivsw.2016.7566604

Revision debug with non-linear version history in regression verification

2016· article· en· W2519113869 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 Testing and Debugging Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDebuggingComputer scienceBottleneckPreprocessorRanking (information retrieval)Regression testingSoftware bugCorrectnessGraphSoftware engineeringProgramming languageTheoretical computer scienceEmbedded systemInformation retrievalSoftware system

Abstract

fetched live from OpenAlex

Modern digital designs are relentlessly growing in complexity, making their verification a daunting task. Verification and debugging are the bottleneck, accounting for up to 70% of the design cycle. Most automated debugging tools target failures in isolation and rely solely on the current version of a design's RTL. A recently developed methodology targets multiple failures simultaneously while leveraging the revision history present in a version control system. It finds revisions likely to be responsible for the failures and ranks them such that higher ranked revisions are more likely to contain bugs. However, this technique treats the version history as a simple linear list of revisions rather than a graph structure. To address this limitation, this paper presents a technique that properly leverages the branching information in version control systems. It offers two-stage ranking with improved performance, allowing both branches and branch-local revisions to be ranked.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.646
Threshold uncertainty score0.189

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 head0.023
GPT teacher head0.253
Teacher spread0.230 · 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