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Found 14 papers, 6 papers with code
LLM-Powered Test Case Generation for Detecting Tricky Bugs
Conventional automated test generation tools struggle to generate test oracles and tricky bug-revealing test inputs.
Exploring the Impact of In-Browser Deep Learning Inference on Quality of User Experience and Performance
Additionally, we noticed that in-browser inference increases the time it takes for graphical user interface (GUI) components to load in web browsers by a significant 67. 2\%, which severely impacts the overall QoE for users of web applications that depend on this technology.
Bias Behind the Wheel: Fairness Analysis of Autonomous Driving Systems
This paper analyzes fairness in automated pedestrian detection, a crucial but under-explored issue in autonomous driving systems.
Fairness Improvement with Multiple Protected Attributes: How Far Are We?
Existing research mostly improves the fairness of Machine Learning (ML) software regarding a single protected attribute at a time, but this is unrealistic given that many users have multiple protected attributes.
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
How many datasets are used for evaluating bias mitigation methods?
A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers
We find that (1) the bias mitigation methods significantly decrease ML performance in 53% of the studied scenarios (ranging between 42%~66% according to different ML performance metrics); (2) the bias mitigation methods significantly improve fairness measured by the 4 used metrics in 46% of all the scenarios (ranging between 24%~59% according to different fairness metrics); (3) the bias mitigation methods even lead to decrease in both fairness and ML performance in 25% of the scenarios; (4) the effectiveness of the bias mitigation methods depends on tasks, models, the choice of protected attributes, and the set of metrics used to assess fairness and ML performance; (5) there is no bias mitigation method that can achieve the best trade-off in all the scenarios.
Learning point embedding for 3D data processing
Among 2D convolutional networks on point clouds, point-based approaches consume point clouds of fixed size directly.
Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub
This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers.
An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications
In contrast, faults related to the deployment of DL models on mobile devices (named as deployment faults of mobile DL apps) have not been well studied.
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Based on the data and the platform, we conduct extensive experiments to compare the performance of state-of-the-art FL algorithms under heterogeneity-aware and heterogeneity-unaware settings.
Understanding Challenges in Deploying Deep Learning Based Software: An Empirical Study
Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software applications.
Software Engineering
SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering
However, commonly used out-of-the-box sentiment analysis tools cannot obtain reliable results on SE tasks and the misunderstanding of technical jargon is demonstrated to be the main reason.
A First Look at Emoji Usage on GitHub: An Empirical Study
We find that emojis are used by a considerable proportion of GitHub users.
Computers and Society Software Engineering
Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification
To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i. e., the source language, usually English) to another language with fewer labels (i. e., the target language).
