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

Prior works have developed transformer-based language learning models to automatically
generate source code for a task without compilation errors. The datasets used to train these …

Data scientists often develop machine learning models to solve a variety of problems in the
industry and academy but not without facing several challenges in terms of Model …

In recent years, Jupyter notebooks have grown in popularity in several domains of software
engineering, such as data science, machine learning, and computer science education …

Artificial Intelligence (AI) and Machine Learning (ML) are pervasive in the current computer
science landscape. Yet, there still exists a lack of software engineering experience and best …

The popularity of machine learning has wildly expanded in recent years. Machine learning
techniques have been heatedly studied in academia and applied in the industry to create …

The rise of machine learning (ML) and its embedding in systems has drastically changed the
engineering of software-intensive systems. Traditionally, software engineering focuses on …

Context In the last decade of data-driven decision-making, Machine Learning (ML) systems
reign supreme. Because of the different characteristics between ML and traditional Software …

Python is a popular programming language characterized by its simple syntax and easy
learning curve. Like many languages, Python has a set of best practices that should be …

The rise of machine learning (ML) for solving current and future problems increased the
production of ML-enabled software systems. Unfortunately, standardized tool chains for …