Search Results for author:
Found 17 papers, 5 papers with code
An Empirical Study on the Fairness of Pre-trained Word Embeddings
Pre-trained word embedding models are easily distributed and applied, as they alleviate users from the effort to train models themselves.
The Quest for Content: A Survey of Search-Based Procedural Content Generation for Video Games
Video games demand is constantly increasing, which requires the costly production of large amounts of content.
Enhancing Genetic Improvement Mutations Using Large Language Models
We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits.
Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
This work will facilitate further advances in DL energy measurement and the development of energy-aware practices for DL systems.
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.
Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances
In this paper, we propose an approach for the selection of the initial seeds to generate IDIs for fairness testing.
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.
Do Not Take It for Granted: Comparing Open-Source Libraries for Software Development Effort Estimation
We carry out a thorough empirical study comparing the performance of the machine learners on 5 SEE datasets in the two most common SEE scenarios (i. e., out-of-the-box-ml and tuned-ml) as well as an in-depth analysis of the documentation and code of their APIs.
On The Effectiveness of One-Class Support Vector Machine in Different Defect Prediction Scenarios
While, we cannot conclude that OCSVM is the best classifier, our results still show interesting findings.
Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study
In the last decade, several studies have explored automated techniques to estimate the effort of agile software development.
A Survey on Machine Learning Techniques for Source Code Analysis
This paper aims to summarize the current knowledge in applied machine learning for source code analysis.
Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies
The results show that strategies generated by Sentinel outperform the baseline strategies in 95% of the cases always with large effect sizes.
Empirical Standards for Software Engineering Research
Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e. g. a questionnaire survey).
Software Engineering General Literature
FrUITeR: A Framework for Evaluating UI Test Reuse
UI testing is tedious and time-consuming due to the manual effort required.
Software Engineering
A Framework for Genetic Algorithms Based on Hadoop
The main purpose of this framework is to allow the user to focus on the aspects of GA that are specific to the problem to be addressed, being sure that this task is going to be correctly executed on the Cloud with a good performance.
