aiqbench analysis
This research addresses a fundamental challenge in AI-assisted debugging by training models to predict nondeterministic execution paths in concurrent Go programs rather than guessing single outcomes. By leveraging scheduler-induced randomness as a positive signal, the study demonstrates that distribution-aware fine-tuning significantly outperforms zero-shot LLMs and standard cross-entropy methods on real-world production bugs from major systems like Kubernetes and CockroachDB. The work provides valuable open-source tooling for improving model calibration in complex software engineering contexts where multiple valid futures exist simultaneously.
When the Next Step Is Not One Step: Distribution-Aware Execution Modeling for Concurrent Go Programs
arXiv:2606.17508v1 Announce Type: new Abstract: Training a model to predict the next step in a concurrent program is harder than it looks: two runs of the same program from the same trace prefix can produce different next events, both valid, because the scheduler is nondeterministic. A model trained against a single label is learning to guess one outcome of a random process. We turn this around and use the nondeterminism as a training signal. We run each program many times, aggregate the observed next events into an empirical distribution, and fine-tune a 7B model to match that distribution with a KL objective. On 798 held-out predictions drawn from real production Go bugs (CockroachDB, Kubernetes, gRPC, etcd), fine-tuning on fewer than a thousand traces reaches 36.2% accuracy, ahead of Gemini 3.5 Flash used zero-shot (34.8%) and the same model without fine-tuning (28.6%). Distribution training matches cross-entropy on accuracy (35.8% vs. 36.2%) while reducing Expected Calibration Error from 0.205 to 0.169. We also derive a formal goroutine-leak signature for a class of select-blocked goroutines where P(GoUnblock)=0 holds by scheduler semantics, not by learning. We release the dataset, trained adapters, and all tooling.
Abstract:Training a model to predict the next step in a concurrent program is harder than it looks: two runs of the same program from the same trace prefix can produce different next events, both valid, because the scheduler is nondeterministic. A model trained against a single label is learning to guess one outcome of a random process. We turn this around and use the nondeterminism as a training signal. We run each program many times, aggregate the observed next events into an empirical distribution, and fine-tune a 7B model to match that distribution with a KL objective. On 798 held-out predictions drawn from real production Go bugs (CockroachDB, Kubernetes, gRPC, etcd), fine-tuning on fewer than a thousand traces reaches 36.2% accuracy, ahead of Gemini 3.5 Flash used zero-shot (34.8%) and the same model without fine-tuning (28.6%). Distribution training matches cross-entropy on accuracy (35.8% vs. 36.2%) while reducing Expected Calibration Error from 0.205 to 0.169. We also derive a formal goroutine-leak signature for a class of select-blocked goroutines where P(GoUnblock)=0 holds by scheduler semantics, not by learning. We release the dataset, trained adapters, and all tooling.
Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Programming Languages (cs.PL); Software Engineering (cs.SE)
D.1.3; D.2.5; I.2.7
arXiv:2606.17508 [cs.LG]
(or arXiv:2606.17508v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.17508 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 294 words