MétaCan
Menu
Back to cohort
Record W1988040396 · doi:10.1109/icst.2012.120

Automated Unit Testing of a SCADA Control Software: An Industrial Case Study Based on Action Research

2012· article· en· W1988040396 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 Calgary
Fundersnot available
KeywordsSCADATest suiteUnit testingRegression testingWhite-box testingComputer scienceKeyword-driven testingTest Management ApproachReliability engineeringTest (biology)SuiteManual testingSoftware engineeringRocket (weapon)SoftwareBlack boxEngineeringTest caseSoftware developmentSoftware constructionOperating systemRegression analysisArtificial intelligenceMachine learningAeronautics

Abstract

fetched live from OpenAlex

We report in this case-study paper our experience and success story with a practical approach and tool for unit regression testing of a SCADA (Supervisory Control and Data Acquisition) software. The tool uses a black-box specification of the units under test to automatically generate NUnit test code. We then improved the test suite by white-box and mutation testing. The approach and tool were developed in an action-research project to test a commercial large-scale SCADA system called Rocket.

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)0.005
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: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.384
GPT teacher head0.438
Teacher spread0.054 · 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

Quick stats

Citations24
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicSoftware Testing and Debugging TechniquesFrench-language works237,207