LDMDroid applies LLMs in a state-aware process to trigger data manipulation functions and uses visual cues to detect errors, finding 17 bugs across 24 Android apps with 14 developer confirmations.
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EditFlow reconstructs temporal developer editing flows from code changes to benchmark and optimize AI code edit recommenders so they align with natural incremental reasoning rather than static snapshots.
A new bias-aware benchmark for duplicate bug report detection shows simpler techniques outperform recent sophisticated methods on most projects and match industry tools.
A survey of 87 agents for computer use and 33 datasets that introduces a three-dimensional taxonomy across domain, interaction, and agent perspectives and identifies six research gaps.
Industry AI practitioners view model quality through nine attributes with context-dependent priorities, where data imbalance is a key challenge addressed by strategies like active learning, as confirmed by interviews and a follow-up survey.
A literature survey of 164 papers on software fairness reveals gaps in requirements engineering, intersectional measures, unstructured data, and white-box ML methods.
citing papers explorer
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LDMDroid: Leveraging LLMs for Detecting Data Manipulation Errors in Android Apps
LDMDroid applies LLMs in a state-aware process to trigger data manipulation functions and uses visual cues to detect errors, finding 17 bugs across 24 Android apps with 14 developer confirmations.
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EditFlow: Benchmarking and Optimizing Code Edit Recommendation Systems via Reconstruction of Developer Flows
EditFlow reconstructs temporal developer editing flows from code changes to benchmark and optimize AI code edit recommenders so they align with natural incremental reasoning rather than static snapshots.
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Duplicate Bug Report Detection: How Far Are We?
A new bias-aware benchmark for duplicate bug report detection shows simpler techniques outperform recent sophisticated methods on most projects and match industry tools.
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A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions
A survey of 87 agents for computer use and 33 datasets that introduces a three-dimensional taxonomy across domain, interaction, and agent perspectives and identifies six research gaps.
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Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions
Industry AI practitioners view model quality through nine attributes with context-dependent priorities, where data imbalance is a key challenge addressed by strategies like active learning, as confirmed by interviews and a follow-up survey.
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Software Fairness: An Analysis and Survey
A literature survey of 164 papers on software fairness reveals gaps in requirements engineering, intersectional measures, unstructured data, and white-box ML methods.