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.

翻译：软件即服务（SaaS）定价模型——涵盖功能特性、使用限制、套餐方案及附加服务——其复杂性呈指数级增长，配置选项已从数十个演变为数千个。这种快速扩张为基于SaaS的信息系统（IS）的开发与运营带来了重大挑战，因为对此类配置进行人工管理不仅耗时、易错，且最终难以为继。新兴的定价驱动DevOps范式旨在通过自动化定价管理任务（例如将面向人类的定价转化为面向机器的定价，或为特定用户需求匹配最优订阅方案）来应对这些问题，从而最终减少人工干预。本文通过提出七种分析操作推进该领域研究，这些操作部分或完全支持上述定价管理任务，从而为定义更专业的新型操作奠定基础。为实现这一目标，我们将机器可读定价映射为约束满足优化问题（CSOP）——一种在类似领域成功应用的方法——借此实现并应用这些操作，以从复杂定价模型中发掘潜在且非平凡的内在规律。所提方法已在基于MiniZinc的参考框架中实现，并通过超过150个定价模型进行测试，在基准测试中识别出35个定价模型的错误。结果表明，该方法在错误识别方面具有显著效果，并具备优化定价驱动DevOps流程的潜力。