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


Iris Figalist


Christoph Elsner


Jan Bosch

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

Helena Holmström Olsson

Malmö university

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
9783030641474 (ISBN)

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

Subject Categories

Other Computer and Information Science

Software Engineering

Computer Systems



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