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Assuring the machine learning lifecycle: Desiderata, methods, and challenges
Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …
successful applications. The potential for this success to continue and accelerate has placed …
Testing machine learning based systems: a systematic map**
Abstract Context: A Machine Learning based System (MLS) is a software system including
one or more components that learn how to perform a task from a given data set. The …
one or more components that learn how to perform a task from a given data set. The …
Software engineering for AI-based systems: a survey
AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …
Machine learning testing: Survey, landscapes and horizons
This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
Collaboration challenges in building ml-enabled systems: Communication, documentation, engineering, and process
The introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …
need for software engineers to collaborate with data scientists and other specialists. While …
A software engineering perspective on engineering machine learning systems: State of the art and challenges
G Giray - Journal of Systems and Software, 2021 - Elsevier
Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …
software development, where algorithms are hard-coded by humans, to ML systems …
Trustworthy artificial intelligence in Alzheimer's disease: state of the art, opportunities, and challenges
Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
remarkable performance in providing medical professionals and patients with support for …
remarkable performance in providing medical professionals and patients with support for …
A meta-summary of challenges in building products with ml components–collecting experiences from 4758+ practitioners
Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …
software-engineering challenges and exacerbates existing ones. Many researchers have …
Analysis and classification of heart rate using CatBoost feature ranking model
Background In specific contexts, it is difficult to manually differentiate Sinus Rhythm (SR),
Sinus Tachycardia (ST), and Atrial Tachycardia (AT) from ECG signals. Upright P-wave is a …
Sinus Tachycardia (ST), and Atrial Tachycardia (AT) from ECG signals. Upright P-wave is a …
[HTML][HTML] CLARUS: an interactive explainable AI platform for manual counterfactuals in graph neural networks
Background: Lack of trust in artificial intelligence (AI) models in medicine is still the key
blockage for the use of AI in clinical decision support systems (CDSS). Although AI models …
blockage for the use of AI in clinical decision support systems (CDSS). Although AI models …