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Found 18 papers, 9 papers with code
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.
Using Protected Attributes to Consider Fairness in Multi-Agent Systems
Fairness in Multi-Agent Systems (MAS) has been extensively studied, particularly in reward distribution among agents in scenarios such as goods allocation, resource division, lotteries, and bargaining systems.
Effi-Code: Unleashing Code Efficiency in Language Models
In this work, we present Effi-Code, an approach to enhancing code generation in LLMs that can improve both efficiency and correctness.
Rethinking the Influence of Source Code on Test Case Generation
Large language models (LLMs) have been widely applied to assist test generation with the source code under test provided as the context.
EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization
To address this issue, we propose \textbf{EffiLearner}, a self-optimization framework that utilizes execution overhead profiles to improve the efficiency of LLM-generated 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.
EffiBench: Benchmarking the Efficiency of Automatically Generated Code
This paper presents EffiBench, a benchmark with 1, 000 efficiency-critical coding problems to assess the efficiency of code generated by code generation models.
AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
The advancement of natural language processing (NLP) has been significantly boosted by the development of transformer-based large language models (LLMs).
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ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair
With the growing interest on Large Language Models (LLMs) for fault localization and program repair, ensuring the integrity and generalizability of the LLM-based methods becomes paramount.
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.
Leveraging Automated Unit Tests for Unsupervised Code Translation
With little to no parallel data available for programming languages, unsupervised methods are well-suited to source code translation.
Machine Learning Testing: Survey, Landscapes and Horizons
This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research.
Model Validation Using Mutated Training Labels: An Exploratory Study
MV mutates training data labels, retrains the model against the mutated data, then uses the metamorphic relation that captures the consequent training performance changes to assess model fit.
