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Found 11 papers, 3 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.
EffiBench: Benchmarking the Efficiency of Automatically Generated Code
This paper presents EffiBench, a benchmark with 1, 000 efficiency-critical coding problems for assessing 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).
Ranked #1 on
Code Generation
on HumanEval
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 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.
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.
