208 citations · 236 across the 11 of their papers we have counts for
7 papers · 1 filter
Bayesian propensity score matching in automotive embedded software engineering
Yuchu Liu, David Issa Mattos, Jan Bosch +2
Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised fiel…
An architecture for enabling A/B experiments in automotive embedded software
Yuchu Liu, Jan Bosch, Helena Holmström Olsson +1
A/B experimentation is a known technique for data-driven product development and has demonstrated its value in web-facing businesses. With the digitalisation of the automotive indu…
Size matters? Or not: A/B testing with limited sample in automotive embedded software
Yuchu Liu, David Issa Mattos, Jan Bosch +2
A/B testing is gaining attention in the automotive sector as a promising tool to measure causal effects from software changes. Different from the web-facing businesses, where A/B t…
On the Assessment of Benchmark Suites for Algorithm Comparison
David Issa Mattos, Lucas Ruud, Jan Bosch +1
Benchmark suites, i.e. a collection of benchmark functions, are widely used in the comparison of black-box optimization algorithms. Over the years, research has identified many des…
On the experiences of adopting automated data validation in an industrial machine learning project
Lucy Ellen Lwakatare, Ellinor Rånge, Ivica Crnkovic +1
Background: Data errors are a common challenge in machine learning (ML) projects and generally cause significant performance degradation in ML-enabled software systems. To ensure e…
Real-time End-to-End Federated Learning: An Automotive Case Study
Hongyi Zhang, Jan Bosch, Helena Holmström Olsson
With the development and the increasing interests in ML/DL fields, companies are eager to apply Machine Learning/Deep Learning approaches to increase service quality and customer e…