Machine learning components are essential for today's software systems, causing a need to
adapt traditional software engineering practices when developing machine-learning-based …

Large Language Model (LLM)-based agents have shown effectiveness across many
applications. However, their use in data science scenarios requiring solving long-term …

Machine Learning (ML) software has been widely adopted in modern society, with reported
fairness implications for minority groups based on race, sex, age, etc. Many recent works …

Deep Neural Networks (DNNs) are used in a wide variety of applications. However, as in
any software application, DNN-based apps are afflicted with bugs. Previous work observed …

Fairness of machine learning (ML) software has become a major concern in the recent past.
Although recent research on testing and improving fairness have demonstrated impact on …

Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding
statistically relevant regularities of patterns in sequentially ordered data. It has been an …

Effective model management is crucial for ensuring performance and reliability in Machine
Learning (ML) systems, given the dynamic nature of data and operational environments …

The increasing number of edge devices with enhanced sensing capabilities, such as
smartphones, wearables, and IoT devices equipped with sensors, holds the potential for …

Today deep learning is widely used for building software. A software engineering problem
with deep learning is that finding an appropriate convolutional neural network (CNN) model …

Machine learning (ML) is increasingly being used in critical decision-making software, but
incidents have raised questions about the fairness of ML predictions. To address this issue …