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Record W2100104470 · doi:10.1109/ccece.2004.1349694

A new approach to test case generation based on real-time process algebra (RTPA)

2004· article· en· W2100104470 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
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceProcess (computing)SoftwareSet (abstract data type)Code (set theory)Reliability engineeringProgramming languageSoftware engineeringEngineering

Abstract

fetched live from OpenAlex

In order to detect and fix errors and bugs in software design and implementation, testing is a vital process in software engineering. It is recognized that testing a large-scale software system needs more intelligence and effort than code design and implementation do. The paper presents a new approach to specification-based test generation that enables test cases to be generated before the implementation of code. We adopt real-time process algebra (RTPA) to describe software system architectures, static and dynamic behaviors. Based on RTPA, a method of least completed set of tests (LCST) is developed, which reveals that the sufficient number of tests for a given software is O(4/sup n/), where n is the number of the input variables. The LCST method provides a new way to predict how many independent test cases exist for a given program, and how the tests may be generated on the basis of its RTPA specifications. Experimental case studies on applications of the LCST method are reported that demonstrate the usage and efficiency of this new method.

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: none
Teacher disagreement score0.789
Threshold uncertainty score0.539

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 head0.026
GPT teacher head0.258
Teacher spread0.232 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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