Increasingly larger number of software systems today are including data science
components for descriptive, predictive, and prescriptive analytics. The collection of data …

Deep learning has gained substantial popularity in recent years. Developers mainly rely on
libraries and tools to add deep learning capabilities to their software. What kinds of bugs are …

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 …

Background. The rapid and growing popularity of machine learning (ML) applications has
led to an increasing interest in MLOps, that is, the practice of continuous integration and …

As a dynamic programming language, Python is widely used in many fields. For developers,
various language features affect programming experience. For researchers, they affect the …

Background: Meeting the growing industry demand for Data Science requires cross-
disciplinary teams that can translate machine learning research into production-ready code …

In software development, the term “technical debt”(TD) is used to characterize short-term
solutions and workarounds implemented in source code which may incur a long-term cost …

The scale of the potential implications of machine learning (ML) has prompted discussions
on the issues of corporate control and technological openness. However, how commercial …

Python is a multi-paradigm programming language that fully supports object-oriented (OO)
programming. The language allows writing code in a non-procedural imperative manner …

Actor concurrency is becoming increasingly important in the development of real-world
software systems. Although actor concurrency may be less susceptible to some …