208 citations · 236 across the 11 of their papers we have counts for
13 papers
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…
The use of incentives to promote Technical Debt management
Terese Besker, Antonio Martini, Jan Bosch
When developing software, it is vitally important to keep the level of technical debt down since it is well established from several studies that technical debt can, e.g., lower th…
HPM-Frame: A Decision Framework for Executing Software on Heterogeneous Platforms
Hugo Andrade, Ola Benderius, Christian Berger +2
Heterogeneous computing is one of the most important computational solutions to meet rapidly increasing demands on system performance. It typically allows the main flow of applicat…
Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions
David Issa Mattos, Jan Bosch, Helena Holmström Olsson
Frequentist statistical methods, such as hypothesis testing, are standard practice in papers that provide benchmark comparisons. Unfortunately, these methods have often been misuse…
Refactoring Software in the Automotive Domain for Execution on Heterogeneous Platforms
Hugo Andrade, Ivica Crnkovic, Jan Bosch
The most important way to achieve higher performance in computer systems is through heterogeneous computing, i.e., by adopting hardware platforms containing more than one type of p…
Software Logging for Machine Learning
Nathan Bosch, Jan Bosch
System logs perform a critical function in software-intensive systems as logs record the state of the system and significant events in the system at important points in time. Unfor…
Engineering AI Systems: A Research Agenda
Jan Bosch, Ivica Crnkovic, Helena Holmström Olsson
Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deployin…
Software Engineering Challenges of Deep Learning
Anders Arpteg, Björn Brinne, Luka Crnkovic-Friis +1
Surprisingly promising results have been achieved by deep learning (DL) systems in recent years. Many of these achievements have been reached in academic settings, or by large tech…