MétaCan
Menu
Back to cohort

Automatic Generation of Parallel Java Programs and their Validation using Combinatorial Testing Suites

2021· article· en· W3174050238 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venue2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) · 2021
Typearticle
Languageen
FieldComputer Science
TopicSoftware Testing and Debugging Techniques
Canadian institutionsnot available
FundersStem Cell Network
KeywordsComputer scienceExecutableJavaCorrectnessProgramming languageTest suiteParallel computingThread (computing)SuiteMulti-core processorOperating systemTest case

Abstract

fetched live from OpenAlex

For using multicore processors at best, parallelism has to be embedded into applications by using threads or processes. In this paper we propose a pair of tools generating a parallel version of a Java program and a test suite for it. Firstly, we have developed a tool capable of transforming a given sequential portion of a Java executable program into a multi-thread version of it. Secondly, an additional tool has been developed as a testing support in order to validate the correctness of the parallel version, by using automated combinatorial testing. For a user-definable set of inputs of the original Java program, the testing tool checks whether the corresponding outputs generated by executing both the original sequential version and the transformed parallel version are the same. The parallelising tool and its validating testing counterpart have been implemented and applied on sample Java programs, and some results are shown in this paper.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.180
GPT teacher head0.319
Teacher spread0.139 · 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