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Found 22 papers, 12 papers with code
Can Agents Fix Agent Issues?
These results underscore the unique challenges of maintaining agent systems compared to traditional software, highlighting the need for further research to develop advanced SE agents for resolving agent issues.
AMQA: An Adversarial Dataset for Benchmarking Bias of LLMs in Medicine and Healthcare
Large language models (LLMs) are reaching expert-level accuracy on medical diagnosis questions, yet their mistakes and the biases behind them pose life-critical risks.
Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness
Finally, KillBadCode purifies the poisoned data by removing all poisoned samples containing the identified trigger tokens.
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes.
Benchmarking Bias in Large Language Models during Role-Playing
Our benchmark reveals 72, 716 biased responses across the studied LLMs, with individual models yielding between 7, 754 and 16, 963 biased responses, underscoring the prevalence of bias in role-playing contexts.
Personality-Guided Code Generation Using Large Language Models
Code generation, the automatic creation of source code from natural language descriptions, has garnered significant attention due to its potential to streamline software development.
Large Language Model-Based Agents for Software Engineering: A Survey
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i. e., LLM-based agents.
LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs
To address this problem, we propose TrickCatcher, an LLM-powered approach to generating test cases for uncovering bugs in plausible programs.
Anatomizing Deep Learning Inference in Web Browsers
The gap on mobile CPU and mobile GPU is 15. 8 times and 7. 8 times, respectively.
A Prompt Learning Framework for Source Code Summarization
Our comprehensive experimental results show that PromptCS significantly outperforms instruction prompting schemes (including zero-shot learning and few-shot learning) on all four widely used metrics, and is comparable to the task-oriented fine-tuning scheme.
Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems
This paper conducts fairness testing of 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).
