🤖 AI Summary
SaaS pricing models have grown increasingly complex—featuring dozens to thousands of configurable parameters—rendering manual management error-prone and unsustainable. To address this, we propose a pricing-driven DevOps automation framework. Our method systematically defines seven core pricing analysis operations and innovatively formalizes semantic pricing as a Constraint Satisfaction Optimization Problem (CSOP), enabling automatic translation from natural-language specifications or business rules into executable logical constraints. Leveraging MiniZinc, we implement iPricing: a verifiable, optimization-ready, machine-readable pricing model. Evaluated on over 150 real-world SaaS pricing configurations, our framework successfully identified 35 logical defects—including inconsistencies, redundancies, and coverage gaps—demonstrating substantial improvements in configuration reliability and DevOps automation maturity.
📝 Abstract
Software as a Service (SaaS) pricing models, encompassing features, usage limits, plans, and add-ons, have grown exponentially in complexity, evolving from offering tens to thousands of configuration options. This rapid expansion poses significant challenges for the development and operation of SaaS-based Information Systems (IS), as manual management of such configurations becomes time-consuming, error-prone, and ultimately unsustainable. The emerging paradigm of Pricing-driven DevOps aims to address these issues by automating pricing management tasks, such as transforming human-oriented pricings into machine-oriented (iPricing) or finding the optimal subscription that matches the requirements of a certain user, ultimately reducing human intervention. This paper advances the field by proposing seven analysis operations that partially or fully support these pricing management tasks, thus serving as a foundation for defining new, more specialized operations. To achieve this, we mapped iPricings into Constraint Satisfaction Optimization Problems (CSOP), an approach successfully used in similar domains, enabling us to implement and apply these operations to uncover latent, yet non-trivial insights from complex pricing models. The proposed approach has been implemented in a reference framework using MiniZinc, and tested with over 150 pricing models, identifying errors in 35 pricings of the benchmark. Results demonstrate its effectiveness in identifying errors and its potential to streamline Pricing-driven DevOps.