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Goal-Driven Reusable Test Case Design

2023· article· en· W4387951206 on OpenAlex
Ibtesam Gwasem, Weichang Du, Andrew McAllister

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
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsComputer scienceTest strategyDomain engineeringSoftware performance testingSoftware engineeringDomain (mathematical analysis)Non-regression testingSystem integration testingFunctional requirementSoftware reliability testingManual testingIntegration testingReliability engineeringSoftware developmentSoftware constructionSoftwareEngineeringOperating system

Abstract

fetched live from OpenAlex

Software non-functional properties (NFPs) play the dominants role for the acceptability of software in the market. As in single software systems, testing NFPs in software product lines is also important to ensure quality of software products. Research in the area of software product lines testing has been very active over the past decade. However, the most focus of this research has been on testing software functional properties, while testing of NFPs has not received much research attention. In this paper we address non-functional requirements testing based on goal models. Specifically, we proposed a methodology for reusable test case design during domain engineering that supports early testing at the domain analysis stage to help create testable non-functional requirements that will be used for designing effective test cases at the domain testing level. We focus on testing of domain core components. A prototype testing system was also developed to support testing based on the proposed methodology.

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)0.002
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 score0.054
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0010.000
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 head0.090
GPT teacher head0.316
Teacher spread0.226 · 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