Automatic Generation of Parallel Java Programs and their Validation using Combinatorial Testing Suites
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it