Measuring What Matters with Jules
A cluster of bugs around "sandbox timeout errors," "broker config failures," and "network isolation flaky tests" all point toward a common aspirational goal like "Strengthen sandbox execution reliability." Individually, each bug is too task-specific to serve as a goal. It successfully captured the primary signal for straightforward engineering problems.Exploration budgets matter: Complex, multi-faceted problems are naturally harder, but giving the agent more resources to investigate pays off. By increasing the exploration budget from two rounds to three, the agent’s Hit@5 accuracy (defined as the rate at which a correct diagnostic insight appears within its top 5 recommendations) rebounded significantly from 33% to 57%.
- ▪A cluster of bugs around "sandbox timeout errors," "broker config failures," and "network isolation flaky tests" all point toward a common aspirational goal like "Strengthen sandbox execution reliability." Individually, each bug is too task
- ▪It successfully captured the primary signal for straightforward engineering problems.Exploration budgets matter: Complex, multi-faceted problems are naturally harder, but giving the agent more resources to investigate pays off.
- ▪By increasing the exploration budget from two rounds to three, the agent’s Hit@5 accuracy (defined as the rate at which a correct diagnostic insight appears within its top 5 recommendations) rebounded significantly from 33% to 57%.
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Leveraging real bug fixes as “ground truth”Based on our work on continuous AI systems at Google Labs, we’ve found that building evaluations capable of grading a proactive agent on its insight policy requires establishing a “ground truth.” One way to build this “ground truth” is to analyze a team’s real bug-fixing history along two heuristics we term temporal proximity and semantic similarity.Our hypothesis is simple: when engineers file and fix several related bugs within a short time period, those bugs are often symptoms of a single underlying engineering effort.
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