Data-driven software design with Constraint Oriented Multi-variate Bandit Optimization (COMBO)

Context Software design in e-commerce can be improved with user data through controlled experiments (i.e. A/B tests) to better meet user needs. Machine learning-based algorithmic optimization techniques extends the approach to large number of variables to personalize software to different user needs. So far the optimization techniques has only been applied to optimize software of low complexity, such as colors and wordings of text. Objective In this paper, we introduce the COMBO toolkit with capability to model optimization variables and their relationship constraints specified through an embedded domain-specific language. The toolkit generates personalized software configurations for users as they arrive in the system, and the configurations improve over time in in relation to some given metric. COMBO has several implementations of machine learning algorithms and constraint solvers to optimize the model with user data by software developers without deep optimization knowledge. Method The toolkit was validated in a proof-of-concept by implementing two features that are relevant to Apptus, an e-commerce company that develops algorithms for web shops. The algorithmic performance was evaluated in simulations with realistic historic user data. Results The validation shows that the toolkit approach can model and improve relatively complex features with many types of variables and constraints, without causing noticeable delays for users. Conclusions We show that modeling software hierarchies in a formal model facilitates algorithmic optimization of more complex software. In this way, using COMBO, developers can make data-driven and personalized software products.