Evaluating Software Architecture: Methods, Models and Examples
Over the past two weeks, I have been diving deep into the world of software architecture evaluation — reading some papers, exploring "actual" case studies and trying to connect abstract concepts to practical scenarios. This article is the result of that journey. I have done my best to break things down clearly and share insights that could be genuinely useful, especially for teams working on complex systems or growing digital platforms like those here in Azerbaijan.
Now. What is Software Architecture? The closest and practically understandable definition of the software architecture I found is given below:
The software architecture of a system is the set of structures needed to reason about the system, which comprise software elements, relations among them, and properties of both.
Architecture evaluations can occur at various stages of software development. They identify strengths and weaknesses among different architectural alternatives during early design, assist in evaluating existing systems before future maintenance or enhancements, and detect architectural drift and erosion.
Software architecture evaluation methods are categorized into four primary groups: experience-based, simulation-based, mathematical modeling-based, and scenario-based. Each method can be employed independently or combined for a comprehensive evaluation.
Experience-based evaluation
Experience-based evaluations leverage previous experience and domain knowledge of the developers or consultants involved. Experts familiar with similar systems can intuitively assess whether a proposed architecture will meet project requirements. Such evaluations utilize pattern recognition, intuition, and heuristics developed through past experiences. However, relying solely on experience can introduce biases or outdated assumptions, potentially misaligning with new project needs.
Imagine, having encountered consistency issues before, a database engineer suggests implementing the CQRS pattern with eventual consistency in a distributed microservices system. However, if the system requires strong consistency - such as in financial transactions - eventual consistency could introduce data integrity issues or conflicts that lead to incorrect balances or transaction failures.
Therac-25
A notable example where professional advice led to significant problems is the Therac-25 incident. The Therac-25 was a computer-controlled radiation therapy machine introduced in the 1980s. Due to software flaws, it delivered massive radiation overdoses, causing severe injuries and deaths. Engineers dismissed user reports, assuming the software was reliable. The incident highlights the dangers of software overconfidence and the need for rigorous testing in safety-critical systems.
To enhance experience-based evaluation, you may combine expert judgment with empirical validation, cross-disciplinary peer reviews, structured risk assessment, industry standard updates, and prioritization of user feedback. In my opinion, structured validation works the best by significantly improving system reliability and safety.
Simulation-based evaluation
Simulation-based evaluations rely on a high level implementation of some or all of the components in the software architecture. The simulation can then be used to evaluate quality requirements such as performance and correctness of the architecture. Simulation can also be combined with prototyping, thus prototypes of an architecture can be executed in the intended context of the completed system.
This approach helps detect design flaws early, optimize efficiency, and ensure system reliability. Methods such as Layered Queuing Networks (LQN) and event-based approaches like RAPIDE have been widely used to evaluate software systems. Research by Balsamo, Di Marco, Inverardi, and Simeoni (2004) and Woodside, Petriu, and Israr (2005) demonstrates how LQN models predict performance bottlenecks, while Luckham and Vera (1995) explore event-based simulation in distributed systems. Case studies on companies like Netflix and Tesla illustrate how simulation optimizes response times and scalability in real-world implementations. Similarly, research by Ramesh and Trivedi (2005) and Marin and Casale (2023) advances LQN methodologies for complex architectural scenarios.
Simulation not only aids in system validation, but also enhances development efficiency by integrating with prototyping techniques. The evolving research in this field continues to refine analytical models, offering practical insights for improving fault tolerance in modern software systems.
The Simian Army
A notable real-world example of simulation-based architecture evaluation that I could find is Netflix's implementation of Chaos Monkey, a tool that randomly disables production instances to test the system's resilience and ability to survive failures without affecting overall service availability. This approach allows Netflix to identify and address potential weaknesses in their microservices architecture, ensuring high availability and scalability.
Mathematical modeling evaluation
Mathematical modeling evaluations involve rigorous mathematical proofs and analytical techniques to assess operational quality requirements of software architectures. Unlike empirical or simulation-based methods, mathematical modeling provides formal validation through quantitative analysis, allowing identification of performance bottlenecks or reliability concerns before the implementation phase.
A common mathematical modeling method is the use of Markov chains and queuing theory, which allows detailed analysis of performance metrics like response time, throughput, and resource utilization. For example, by modeling a software system as a queuing network, it becomes possible to derive exact or approximate solutions to predict performance under different workloads and resource constraints. This helps architects choose optimal configurations and make informed trade-offs during design.
Fuzzy logic
Recent advancements include the integration of fuzzy logic into mathematical modeling, allowing for the handling of uncertainties and vagueness inherent in architectural evaluations. Fuzzy logic-based evaluations enhance traditional mathematical methods by enabling more realistic modeling of subjective quality requirements such as usability and maintainability, complementing operational metrics like performance and reliability.
A notable application of mathematical modeling using fuzzy logic is presented in the paper titled "A Fuzzy Logic-Based Quality Model for Identifying Microservices with Low Maintainability" by Rahime Yilmaz and Feza Buzluca. The model operates by first collecting low-level code metrics (e.g., cyclomatic complexity, lines of code, coupling) from microservices. These crisp numerical values are then translated into fuzzy sets using membership functions that define qualitative states like “low”, “medium” and “high”. Through a fuzzy inference system, these inputs are mapped to higher-level quality sub-characteristics such as modifiability and testability, using a set of expert-defined fuzzy rules. The outputs of these rules are combined and defuzzified to yield a precise maintainability score for each microservice. This layered transformation from quantitative data to qualitative interpretation and back to crisp results allows the model to effectively handle vagueness and uncertainty in architectural quality assessment.
The image below presents the experimental results of the model applied to the so-called "Train Ticket" project of the paper authors.
Scenario-based evaluation
Scenario-based architecture evaluation tries to evaluate a particular quality attribute by creating a scenario profile that forces a very concrete description of the quality requirement. The scenarios from the profile are then used to step through the software architecture and the consequences of the scenario are documented.
There are typically three types of scenarios used:
- Stimulus-response scenarios: Describe how the system responds to internal or external events (e.g., a user request, a server failure).
- Growth scenarios: Reflect potential future changes in functionality or scale, such as adding a new module.
- Exploratory scenarios: Used to explore unknowns or edge cases, like how the system reacts to a network partition.
In practice, scenario-based evaluation involves eliciting scenarios from stakeholders that are relevant to key quality attributes, then describing each scenario in concrete terms, including the stimuli, environment, expected responses, and success measures. The architecture is then walked through using these scenarios—either manually or with supporting tools—to predict or assess the system’s behavior. Finally, the impact of each scenario is documented, including any trade-offs or architectural limitations that surface during the evaluation.
A real-life example of a scenario-based evaluation could be imagined in the context of the "myGov" platform. Although there is no publicly documented scenario-based evaluation applied to myGov, we can conceptualize how such an evaluation could be useful. For instance, a scenario might examine: "How will the system respond if 50,000 users simultaneously attempt to access vaccination records during a public health announcement?" or "What happens if a new government agency wants to integrate its services within two weeks without disrupting existing workflows?". Walking through these scenarios would allow architects and decision-makers to evaluate scalability and modifiability, helping shape a flexible architecture for nationwide digital service delivery.
Conclusion
At the end, software architecture evaluation definitely plays a pivotal role in ensuring that systems are not just functional. But resilient. Scalable. Aligned with stakeholder expectations. So, by exploring aforementioned experience-, simulation-based and other methods, you can comprehensively assess various quality attributes across different development stages.
Obviously, each method offers unique pros — experience provides intuition, simulation offers foresight, mathematics delivers precision, and scenarios reveal real-world readiness. When thoughtfully combined, I am sure that these methods may equip your dev team to make informed decisions, mitigate risks and build systems that perform reliably under evolving demands. Lastly, I would like to emphasize that in the context of growing digital infrastructures (especially in Azerbaijan) continuously adopting these evaluation strategies is essential for sustainable digital transformation.
References
- Netflix Technology Blog. The Netflix Simian Army.
- Michael Mattsson, Håkan Grahn, and Frans Mårtensson. Software Architecture Evaluation Methods for Performance, Maintainability, Testability, and Portability.
- Mahdi Sahlabadi, Ravie Chandren Muniyandi, Zarina Shukur and Faizan Qamar. Lightweight Software Architecture Evaluation for Industry: A Comprehensive Review.
- Zimmermann, H.-J. (2001). Fuzzy Set Theory and Its Applications. Springer.
- Woodside, C., Petriu, D. C., & Israr, T. (2005). "The Future of Software Performance Engineering." In Proceedings of the International Conference on Software Engineering.
- Ramesh, V. & Trivedi, K. S. (2005). "Performance Modeling of Layered Software Architectures." ACM SIGMETRICS Performance Evaluation Review.
- Bass, L., Clements, P., & Kazman, R. (2012). Software Architecture in Practice (3rd ed.). Addison-Wesley.
- Clements, P., Kazman, R., & Klein, M. (2002). Evaluating Software Architectures: Methods and Case Studies. Addison-Wesley.
- Kazman, R., Abowd, G., Bass, L., & Clements, P. (1996). "Scenario-Based Analysis of Software Architecture." IEEE Software.
