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 …
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 …
Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting
SM Cabaneros, B Hughes - Environmental Modelling & Software, 2022 - Elsevier
The use of data-driven techniques such as artificial neural network (ANN) models for
outdoor air pollution forecasting has been popular in the past two decades. However …
outdoor air pollution forecasting has been popular in the past two decades. However …
[HTML][HTML] On the use of deep learning in software defect prediction
Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
A survey on machine learning techniques for source code analysis
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
Digital transformation of cancer care in the era of big data, artificial intelligence and data-driven interventions: navigating the field
Objectives To navigate the field of digital cancer care and define and discuss key aspects
and applications of big data analytics, artificial intelligence (AI), and data-driven …
and applications of big data analytics, artificial intelligence (AI), and data-driven …
Operationalizing machine learning models: A systematic literature review
AB Kolltveit, J Li - Proceedings of the 1st Workshop on Software …, 2022 - dl.acm.org
Deploying machine learning (ML) models to production with the same level of rigor and
automation as traditional software systems has shown itself to be a non-trivial task, requiring …
automation as traditional software systems has shown itself to be a non-trivial task, requiring …
[HTML][HTML] A survey on machine learning techniques applied to source code
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
Machine learning-enabled healthcare information systems in view of Industrial Information Integration Engineering
MP Uysal - Journal of Industrial Information Integration, 2022 - Elsevier
Recent studies on Machine learning (ML) and its industrial applications report that ML-
enabled systems may be at a high risk of failure or they can easily fall short of business …
enabled systems may be at a high risk of failure or they can easily fall short of business …
Effort and Cost Estimation Using Decision Tree Techniques and Story Points in Agile Software Development
E Rodríguez Sánchez, EF Vázquez Santacruz… - Mathematics, 2023 - mdpi.com
Early effort estimation is important for efficiently planning the use of resources in an
Information Technology (IT) project. However, limited research has been conducted on the …
Information Technology (IT) project. However, limited research has been conducted on the …