Data Management Challenges for Deep Learning. - Aiswarya Munappy; Jan Bosch; Helena Holmström Olsson; Anders Arpteg; Björn Brinne (SEAA, 2019)

A Taxonomy of Software Engineering Challenges for Machine Learning Systems: An Empirical Investigation. - Lucy Ellen Lwakatare; Aiswarya Raj; Jan Bosch; Helena Holmström Olsson; Ivica Crnkovic (XP, 2019)

Towards Automated Detection of Data Pipeline Faults. - Aiswarya Raj Munappy; Jan Bosch; Helena Holmström Olsson; Tian J. Wang (APSEC, 2020)

Modelling Data Pipelines. - Aiswarya Raj; Jan Bosch; Helena Holmström Olsson; Tian J. Wang (SEAA, 2020)

From Ad-Hoc Data Analytics to DataOps. - Aiswarya Raj Munappy; David Issa Mattos; Jan Bosch; Helena Holmström Olsson; Anas Dakkak (ICSSP, 2020)

Data Pipeline Management in Practice: Challenges and Opportunities. - Aiswarya Raj Munappy; Jan Bosch; Helena Holmström Olsson (PROFES, 2020)

Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions. - Lucy Ellen Lwakatare; Aiswarya Raj; Ivica Crnkovic; Jan Bosch; Helena Holmström Olsson (Inf. Softw. Technol., 2020)

On the Impact of ML use cases on Industrial Data Pipelines. - M. Aiswarya Raj; Jan Bosch; Helena Holmström Olsson; Anders Jansson 002 (APSEC, 2021)

On the Trade-off Between Robustness and Complexity in Data Pipelines. - Aiswarya Raj Munappy; Jan Bosch; Helena Holmström Olsson (QUATIC, 2021)

Customer Support In The Era of Continuous Deployment: A Software-Intensive Embedded Systems Case Study. - Anas Dakkak; Aiswarya Raj Munappy; Jan Bosch; Helena Holmström Olsson (COMPSAC, 2022)

Data management for production quality deep learning models: Challenges and solutions. - Aiswarya Raj Munappy; Jan Bosch; Helena Holmström Olsson; Anders Arpteg; Björn Brinne (J. Syst. Softw., 2022)

Maturity Assessment Model for Industrial Data Pipelines. - M. Aiswarya Raj; Jan Bosch; Helena Holmström Olsson (APSEC, 2023)