The first systematic review of routine computing synthesizes literature into a taxonomy of temporal, behavioral, cognitive, and variability aspects, outlining applications in health, accessibility, and adaptive support along with persistent challenges.
2, 6 [Now19] NOWACKM.: Fine-grain memory object representation in symbolic execution
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Empirical comparison on 10 Node.js apps finds only 22% overlap between history-based and dynamic change-impact candidates, with dynamic analysis more precise and history adding missed candidates, supporting hybrid use.
Symetra uses visual overviews and group comparison tools to help experts tune symbolic execution parameters, achieving higher branch coverage and faster tuning than fully automated methods.
Empirical comparison of Outlierness, Diversity, Representativeness, Uncertainty, and Random selection for trajectory data augmentation across four datasets shows conditional gains in stability over random baselines but degradation in dense data.
citing papers explorer
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Routine Computing: A Systematic Review of Sensing Daily Life Dimensions Towards Human-Centered Goals
The first systematic review of routine computing synthesizes literature into a taxonomy of temporal, behavioral, cognitive, and variability aspects, outlining applications in health, accessibility, and adaptive support along with persistent challenges.
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Change Impact Recommendation for JavaScript: Lessons from History and Runtime Analysis
Empirical comparison on 10 Node.js apps finds only 22% overlap between history-based and dynamic change-impact candidates, with dynamic analysis more precise and history adding missed candidates, supporting hybrid use.
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Symetra: Visual Analytics for the Parameter Tuning Process of Symbolic Execution Engines
Symetra uses visual overviews and group comparison tools to help experts tune symbolic execution parameters, achieving higher branch coverage and faster tuning than fully automated methods.
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A Systematic Approach for Selecting Trajectories for Data Augmentation
Empirical comparison of Outlierness, Diversity, Representativeness, Uncertainty, and Random selection for trajectory data augmentation across four datasets shows conditional gains in stability over random baselines but degradation in dense data.