An End-to-End Framework for Productive Use of Machine Learning in Software Analytics and Business Intelligence Solutions
Paper i proceeding, 2020

Nowadays, machine learning (ML) is an integral component in a wide range of areas, including software analytics (SA) and business intelligence (BI). As a result, the interest in custom ML-based software analytics and business intelligence solutions is rising. In practice, however, such solutions often get stuck in a prototypical stage because setting up an infrastructure for deployment and maintenance is considered complex and time-consuming. For this reason, we aim at structuring the entire process and making it more transparent by deriving an end-to-end framework from existing literature for building and deploying ML-based software analytics and business intelligence solutions. The framework is structured in three iterative cycles representing different stages in a model’s lifecycle: prototyping, deployment, update. As a result, the framework specifically supports the transitions between these stages while also covering all important activities from data collection to retraining deployed ML models. To validate the applicability of the framework in practice, we compare it to and apply it in a real-world ML-based SA/BI solution.

Business intelligence

Software analytics

Machine learning

Författare

Iris Figalist

Siemens

Christoph Elsner

Siemens

Jan Bosch

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for People, Architecture, Requirements and Traceability

Helena Holmström Olsson

Malmö universitet

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 12562 LNCS 217-233

21st International Conference on Product-Focused Software Process Improvement, PROFES 2020
Turin, Italy,

Ämneskategorier

Annan data- och informationsvetenskap

Programvaruteknik

Datorsystem

DOI

10.1007/978-3-030-64148-1_14

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2021-08-24