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Record W4320005455 · doi:10.1109/tse.2023.3242588

Trace Diagnostics for Signal-Based Temporal Properties

2023· article· en· W4320005455 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Software Engineering · 2023
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsMcMaster UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsTRACE (psycholinguistics)Computer scienceDigital subscriber lineProperty (philosophy)Context (archaeology)Domain-specific languageSpecification languageComplement (music)Medical diagnosisSIGNAL (programming language)Programming languageRoot causeTheoretical computer scienceReliability engineering

Abstract

fetched live from OpenAlex

Trace checking is a verification technique widely used in Cyber-physical system (CPS) development, to verify whether execution traces satisfy or violate properties expressing system requirements. Often these properties characterize complex signal behaviors and are defined using domain-specific languages, such as SB-TemPsy-DSL, a pattern-based specification language for signal-based temporal properties. Most of the trace-checking tools only yield a Boolean verdict. However, when a property is violated by a trace, engineers usually inspect the trace to understand the cause of the violation; such manual diagnostic is time-consuming and error-prone. Existing approaches that complement trace-checking tools with diagnostic capabilities either produce low-level explanations that are hardly comprehensible by engineers or do not support complex signal-based temporal properties. In this paper, we propose <i>TD-SB-TemPsy</i> , a trace-diagnostic approach for properties expressed using SB-TemPsy-DSL. Given a property and a trace that violates the property, <i>TD-SB-TemPsy</i> determines the root cause of the property violation. <i>TD-SB-TemPsy</i> relies on the concepts of <i>violation cause</i> , which characterizes one of the behaviors of the system that may lead to a property violation, and <i>diagnoses</i> , which are associated with violation causes and provide additional information to help engineers understand the violation cause. As part of <i>TD-SB-TemPsy</i> , we propose a language-agnostic methodology to define violation causes and diagnoses. In our context, its application resulted in a catalog of 34 violation causes, each associated with one diagnosis, tailored to properties expressed in SB-TemPsy-DSL. We assessed the applicability of <i>TD-SB-TemPsy</i> on two datasets, including one based on a complex industrial case study. The results show that <i>TD-SB-TemPsy</i> could finish within a timeout of 1 min for <inline-formula><tex-math notation="LaTeX">$\approx 83.66\%$</tex-math></inline-formula> of the trace-property combinations in the industrial dataset, yielding a diagnosis in <inline-formula><tex-math notation="LaTeX">$\approx 99.84\%$</tex-math></inline-formula> of these cases; moreover, it also yielded a diagnosis for all the trace-property combinations in the other dataset. These results suggest that our tool is applicable and efficient in most cases.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.389
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.259
Teacher spread0.217 · 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