Bibliographic record
Abstract
Deep learning compilers play an increasingly important role in implementing learned algorithms efficiently.These compilers are commonly implemented using a set of different programming languages: languages suitable for manipulating high-level tensor graph representations differ from those used to implement efficient low-level operations on accelerator devices.Finding faults in these compilers remains a challenging problem, and previously proposed fault localization techniques have limitations when working with a multilingual codebase.To overcome the aforementioned limitations, this thesis proposes a multilingual fault localization technique based on a language-independent approach to mutant generation.We evaluated this technique using eleven real faults in a deep learning compiler codebase.The results of the empirical evaluation show that the proposed approach can precisely locate four of the eleven faults and correctly ranks the faulty elements as the most suspicious.i B MFL Mutation Operators 88 C DLA Fault Localization Results 90 vi List of Tables 3.1 Notation for spectra metrics collected during program execution. . . .3.2 Notation for mutant metrics computed from mutant test results. . . .5.1 Suspiciousness scores of highest-ranking mutants for defective mid programs in different languages. . . . . . . . . . . . . . . . . . . . . .5.2 EXAM scores for MFL technique on L0 example faults. . . . . . . . .6.1 Summary of the DLA codebase showing line counts grouped by programming language. . . . . . . . .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".