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Record W2733392061 · doi:10.5555/3105427.3105436

JTeXpert at the SBST 2017 tool competition

2017· article· en· W2733392061 on OpenAlexaff
Abdelilah Sakti, Gilles Pesant, Yann‐Gaël Guéhéneuc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Testing and Debugging Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTest suiteUnit testingSuiteCONTESTComputer scienceJavaProgramming languageSoftwareCode (set theory)Competition (biology)Software testingSource codeTest (biology)Test caseArtificial intelligenceSoftware engineeringSet (abstract data type)Machine learning

Abstract

fetched live from OpenAlex

JTeXpert is a software testing tool that automatically generates a whole test suite to satisfy the branch-coverage criterion. It takes as inputs a Java source code and its dependencies and automatically produces a test-case suite in JUnit format. In this paper, we summarize our results for the Unit Testing Tool Competition held at the fifth SBST Contest, where JTeXpert received 849 points and was ranked second. We also analyze our tool's performance.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.032
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.004
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0030.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0160.010

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 head0.041
GPT teacher head0.299
Teacher spread0.258 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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