Automated Analysis of Pricings in SaaS-based Information Systems
Alejandro García-Fernández, José Antonio Parejo, Pablo Trinidad, Antonio Ruiz-Cortés
TL;DR
The paper addresses the growing complexity of SaaS pricing models and the need for Pricing-driven DevOps to automate pricing management. It formalizes machine-oriented pricings, iPricings, as Constraint Satisfaction Optimization Problems (CSOPs) and proposes seven analysis operations to derive actionable insights, implemented via MiniZinc and instantiated from Pricing2Yaml specifications. A formal semantics is provided, along with a modular toolchain and datasets (162 real pricings with 35 inconsistencies and a synthetic benchmark), including a Pricing2Yaml editor in SPHERE for real-time error detection. The work demonstrates that constraint programming can effectively identify pricing inconsistencies, support configuration-space analysis, and enable optimal subscription selection, offering a scalable foundation for automated pricing management in SaaS ecosystems.
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.