OptDetect identifies low-optimization native libraries in Android apps with 81.9% real-world accuracy, finding the issue in 30.5% of libraries across 91.7% of top apps, with fixes yielding 10-63% CPU reduction.
Modern code reviews in open-source projects: which problems do they fix?
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Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.
LLM-based Go code review reaches 28% refinement exact match by switching to issue-list generation with neighboring and co-change context augmentation, outperforming primary-issue baseline and CodeReviewer.
citing papers explorer
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Source-Free Detection and Impact Analysis of Compiler Optimization Problems in Mobile Applications
OptDetect identifies low-optimization native libraries in Android apps with 81.9% real-world accuracy, finding the issue in 30.5% of libraries across 91.7% of top apps, with fixes yielding 10-63% CPU reduction.
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Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code
Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.
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Improving LLM-Based Go Code Review through Issue-List Generation and Context Augmentation
LLM-based Go code review reaches 28% refinement exact match by switching to issue-list generation with neighboring and co-change context augmentation, outperforming primary-issue baseline and CodeReviewer.