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Record W2144667363 · doi:10.1145/1145735.1145741

Tool support for randomized unit testing

2006· article· en· W2144667363 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 institutionsWestern University
Fundersnot available
KeywordsComputer scienceJavaUnit testingClass (philosophy)Unit (ring theory)Programming languageSoftwareMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

There are several problem areas that must be addressed when ap-plying randomization to unit testing. As yet no general, fully au-tomated solution that works for all units has been proposed. We therefore have developed RUTE-J, a Java package intended to help programmers do randomized unit testing in Java. In this paper, we describe RUTE-J and illustrate how it supports the development of per-unit solutions for the problems of randomized unit testing. We report on an experiment in which we applied RUTE-J to the standard Java TreeMap class, measuring the efficiency and effec-tiveness of the technique. We also illustrate the use of randomized testing in experimentation, by adapting RUTE-J so that it gener-ates randomized minimal covering test suites, and measuring the effectiveness of the test suites generated.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.288
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.033
GPT teacher head0.276
Teacher spread0.242 · 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

Citations57
Published2006
Admission routes1
Has abstractyes

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