Automated Analysis of Pricings in SaaS-based Information Systems

• URL: http://arxiv.org/abs/2503.21444v1
• Date: Thu, 27 Mar 2025 12:36:57 GMT
• Title: Automated Analysis of Pricings in SaaS-based Information Systems
• Authors: Alejandro García-Fernández, José Antonio Parejo, Pablo Trinidad, Antonio Ruiz-Cortés,
• Abstract summary: This paper advances the field by proposing seven analysis operations that partially or fully support these pricing management tasks.<n>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.
• Score: 42.8610435437513
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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