Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions

LE Lwakatare, A Raj, I Crnkovic, J Bosch… - Information and software …, 2020‏ - Elsevier
Background: Develo** and maintaining large scale machine learning (ML) based
software systems in an industrial setting is challenging. There are no well-established …

Engineering ai systems: A research agenda

J Bosch, HH Olsson, I Crnkovic - Artificial intelligence paradigms for …, 2021‏ - igi-global.com
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 …

Heterogeneous defect prediction

J Nam, S Kim - Proceedings of the 2015 10th joint meeting on …, 2015‏ - dl.acm.org
Software defect prediction is one of the most active research areas in software engineering.
We can build a prediction model with defect data collected from a software project and …

Tuning for software analytics: Is it really necessary?

W Fu, T Menzies, X Shen - Information and Software Technology, 2016‏ - Elsevier
Context: Data miners have been widely used in software engineering to, say, generate
defect predictors from static code measures. Such static code defect predictors perform well …

Defect prediction from static code features: current results, limitations, new approaches

T Menzies, Z Milton, B Turhan, B Cukic, Y Jiang… - Automated Software …, 2010‏ - Springer
Building quality software is expensive and software quality assurance (QA) budgets are
limited. Data miners can learn defect predictors from static code features which can be used …

An investigation on the feasibility of cross-project defect prediction

Z He, F Shu, Y Yang, M Li, Q Wang - Automated Software Engineering, 2012‏ - Springer
Software defect prediction helps to optimize testing resources allocation by identifying defect-
prone modules prior to testing. Most existing models build their prediction capability based …

Chapter 12 The Evolution of Continuous Experimentation in Software Product Development: From Data to a Data-Driven Organization at Scale

A Fabijan, P Dmitriev, J Bosch… - … Digital Transformation: 10 …, 2022‏ - Springer
Software development companies are increasingly aiming to become data-driven by trying
to continuously experiment with the products used by their customers. Although familiar with …

Better cross company defect prediction

F Peters, T Menzies, A Marcus - 2013 10th working conference …, 2013‏ - ieeexplore.ieee.org
How can we find data for quality prediction? Early in the life cycle, projects may lack the data
needed to build such predictors. Prior work assumed that relevant training data was found …

Balancing privacy and utility in cross-company defect prediction

F Peters, T Menzies, L Gong… - IEEE Transactions on …, 2013‏ - ieeexplore.ieee.org
Background: Cross-company defect prediction (CCDP) is a field of study where an
organization lacking enough local data can use data from other organizations for building …

Bellwethers: A baseline method for transfer learning

R Krishna, T Menzies - IEEE Transactions on Software …, 2018‏ - ieeexplore.ieee.org
Software analytics builds quality prediction models for software projects. Experience shows
that (a) the more projects studied, the more varied are the conclusions; and (b) project …