Applicability of Supervised Machine Learning for CI Configuration Selection
Examensarbete för masterexamen
Data science and AI (MPDSC), MSc
This study introduces a novel supervised machine learning (ML) model for accurately assigning CI configurations to test specifications. Current solutions to optimize selection of CI configurations lack the ability to select CI configurations for individual test cases and assigning them into predefined CI configurations. The model employs an ensemble architecture with three sub-models and a rule-based component, each focusing on specific aspects of the problem. Extensive model analysis reveals important features that contribute to the assignment process. A decision support system based on the ML model is developed to evaluate the applicability of supervised ML in CI configuration assignment, validated through a survey study involving domain experts. The study demonstrates that supervised ML can exceed the performance requirements of domain experts. Certain features in test specifications are found to be influential in the assignment outcome. Implementing supervised ML brings business value, reducing misassignments, saving time, and reducing fault slip through. Proposed future research includes exploring fully automated CI configuration assignments and investigating more complex ML models, such as neural networks, for enhanced performance and exploring the potential for fully automated adaptation.
Software testing , continuous integration , continuous integration configuration , supervised machine learning