Guide to Evaluating Software Architecture Characteristics

Guide

Understanding the architectural characteristics of software is crucial for building or modernizing successful applications. This guide examines the essential elements for evaluating software architecture and provides practical insights for developers, architects, and technical leaders.

Evaluating Software Architecture Characteristics - Header

If software architecture defines the structure of an application, then software architecture characteristics are the non-functional requirements that shape a software system’s behavior, performance, and overall success. They go beyond basic functionality and define how well a system performs its intended purpose.

Software architecture characteristics are fundamental to software development because they directly impact the system’s ability to meet business objectives, user needs, and technical requirements. Poor architectural decisions early in development can lead to significant technical debt and costly remediation efforts later. Also, they affect everything from user satisfaction and retention to operational costs and market competitiveness.

When CoStrategix is asked to evaluate the current state of a software product, we evaluate and document the software architecture characteristics. This is the evaluation process that we use.

1. Review Core Characteristics

Performance and Scalability

Software Architecture: Core Characteristics - graphicPerformance metrics are critical indicators of a system’s effectiveness. Response time and throughput must be carefully monitored and optimized to ensure user satisfaction. Systems should be designed to handle increased load through horizontal or vertical scaling, while maintaining optimal resource utilization across computing resources.

In our architectural recommendations, we consider both the historic and estimated usage levels for a software product.

  • For backend services, solutions can involve load balancers and scalable pools of servers that can expand and contract to accommodate usage. Caching services are also important tools for reducing server loads.
  • For front-end code and static assets, content delivery networks can be involved to ensure quick responses for targeted geographic areas. Many other services can also help improve performance for specific use cases.

Reliability, Availability, and Recoverability

Modern systems must maintain high uptime to meet user expectations and business requirements. This involves implementing robust fault handling mechanisms and meeting or exceeding defined Service Level Agreements (SLAs). Monitoring tools and redundancy measures play crucial roles in maintaining system reliability.

Load balancing technologies are also frequently recommended as part of the Reliability and Availability solution for a software product. They can be configured to evaluate the health of the servers that deliver the site and avoid ones with issues. Cloud technologies allow solutions that can increase the reliability and availability by distributing traffic across different data centers or regions in the event of large outages.

Recoverability addresses the meeting of Recovery Time Objectives (RTO) and Recovery Point Objective (RPO) of a system. RTO is the maximum acceptable amount of time that it can take to bring the system back online after major outages. RPO is the maximum amount of data that can be lost during an outage. An example of these goals for a low-traffic financial transaction system may include a longer RTO (due to low traffic) but a near-zero RPO because it is critical to lose none of the transactions.

Security and Compliance

Security cannot be an afterthought in modern software architecture. Comprehensive data protection measures, including encryption and secure data handling practices, must be implemented. Strong authentication and authorization mechanisms need to be in place, and all relevant regulatory requirements must be met and continuously monitored.

In the solution design, we first identify the different roles that need access to the system and then determine the authentication and authorization mechanisms to be used. We look for opportunities to work with organizations’ existing mechanisms and processes when it is a good fit. We also work to ensure the use of industry best practices, such as two-factor authentication. Establishing role-based authorization policies can help promote easy maintainability and grant the appropriate access for different parties, using the principle of least access. In addition to users’ access, solutions should consider external threat mitigation technologies, employing tools that scan for vulnerabilities in different tiers of the application.

Compliance can be specific to a department, organization, industry, or jurisdiction. Additionally, there are general best practices to follow. For instance, one area frequently covered in compliance requirements is Data Retention. In our solutions, we take these requirements into consideration, recommending databases and storage products that allow for soft deletes, versioning, and configurable retention policies. Cloud providers offer robust tools for policy and compliance management for many aspects of a solution and organization.

Time, Budget, and Cost Attribution

While not considered a traditional core “software architecture characteristic,” timeline and budget underlie every solution design. It is important to make – and document – the appropriate trade-offs. The architectural characteristics for a small proof of concept or minimum viable product for a start-up would be very different from those for a large enterprise or medical application with life-or-death criticality.

Additionally, it’s always important to monitor the costs of cloud solutions to make sure that the solution remains cost-effective over time. In a large organization, it can be difficult to track costs, so consider a tagging strategy to help organize resources for cost attribution. Cloud providers offer services for monitoring costs in real-time, allowing you to configure alerts when services reach budget thresholds.

2. Analyze Operational Characteristics

Software Architecture Characteristics - MaintainabilityMaintainability ensures the long-term health of the system. This includes monitoring code quality metrics, maintaining comprehensive documentation, and actively managing technical debt to prevent it from accumulating to unmanageable levels.

Testability refers to how easily software components can be tested for defects and verified for correct functionality. It’s a critical architectural characteristic that enables comprehensive testing through well-designed interfaces, modular components, and appropriate test hooks. Good testability reduces development cycles, improves code quality, and helps catch issues early in the development process.

Deployability is essential for modern software systems. Implement Continuous Integration/Continuous Deployment (CI/CD) pipelines to ensure smooth and reliable deployments. Release management processes must be well-defined, and environment consistency must be maintained across development, testing, and production.

3. Prioritize User-Focused Characteristics

Usability focuses on the overall user experience, including interface design, accessibility features, and perceived performance. These elements significantly impact user satisfaction and adoption rates.

Learnability determines how quickly users can become proficient with the system. This involves creating intuitive user onboarding experiences, maintaining high-quality documentation, and ensuring interface consistency across the application.

Usability and Learnability are both directly tied to the human experience with the software. Technical architecture decisions can contribute to these factors (making sure the interface is fast, etc.), but the true craft in this area comes from our Product and UX Design teams, who work with users to understand business requirements and design intuitive interfaces.

4. Address AI-Specific Characteristics

Software Architecture Characteristics - AI-SpecificWhen evaluating software architecture for AI projects, several unique characteristics require special consideration:

Model Performance and Accuracy

AI systems must balance model accuracy with computational efficiency. This includes monitoring prediction quality, model drift, and retraining requirements. The architecture should support continuous model evaluation and updates without disrupting system operations.

Data Management and Processing

AI architectures require robust data pipelines for collecting, cleaning, and preprocessing large volumes of data. The system must handle both training and inference data efficiently, with considerations for data versioning, lineage tracking, and storage optimization.

Scalability for Training and Inference

AI systems often need different scaling approaches for training versus inference. Training may require burst capacity for periodic intensive computations, while inference might need consistent, low-latency processing capabilities

Explainability and Transparency

Modern AI systems must incorporate mechanisms for model interpretability and decision explanation. This includes logging model inputs, outputs, and decision factors for audit trails and regulatory compliance.

Ethics and Bias Management

Architectural considerations must include systems for monitoring and mitigating algorithmic bias, ensuring fair treatment across different user groups, and maintaining ethical AI practices.

Hardware Optimization

AI architectures often require specialized hardware configurations (GPUs, TPUs, etc.). You must design them to efficiently use these resources while still managing costs.

Best practices specific to AI architectures include:

  • Implementing robust monitoring for model performance and data quality
  • Designing flexible pipelines that can accommodate different model architectures
  • Building systems for easy model deployment and rollback
  • Including comprehensive logging and tracking for model decisions
  • Establishing clear procedures for model updates and versioning

5. Apply the Right Evaluation Methods

Evaluating-Software-Architecture-Characteristics_inset-evalutation-methodsIt is important to determine not only what architectural characteristics are important to measure, but also how to measure them. Often this involves looking at both quantitative and qualitative information.

Quantitative metrics provide objective measures of system performance, including specific benchmarks, error rates, and user satisfaction scores. These metrics should be regularly collected and analyzed to identify trends and areas for improvement. In our solution designs, we often include tools for monitoring performance, computing resources, and other quantitative metrics. In addition to reporting and reviews, this information can also be leveraged to create real-time alerts of conditions that could require immediate attention.

Qualitative assessment involves regular code reviews, architecture reviews, and analysis of user feedback. These subjective evaluations often reveal insights that may not be captured by quantitative metrics alone.

6. Implement Ongoing Monitoring

Successful evaluation requires comprehensive monitoring systems that track key metrics in real-time. Establish measurement frameworks to consistently evaluate architectural characteristics across different components and systems.

Setting up regular evaluation schedules ensures continuous monitoring and improvement, while defining improvement processes helps teams act on evaluation findings effectively.

7. Solve Common Challenges

Software Architecture Characteristics - Challenges & SolutionsTeams often struggle with balancing competing characteristics – for example, performance versus security, or scalability versus cost. Resource constraints can limit implementation options, while technical limitations may require creative solutions or architectural compromises. When you make decisions and compromises, it is important to document your rationale so team members will be able to understand the trade-offs.

Legacy system considerations often complicate architectural decisions, requiring careful planning and gradual modernization approaches. As part of our software modernization practice, when we work with a legacy system, we document the current state before beginning work on architectural changes, thereby building an understanding of possible constraints.

 

In conclusion, evaluating software architecture characteristics is a complex but essential process that requires careful planning, consistent monitoring, and continuous improvement. By following the practices outlined in this guide, teams can better ensure their systems meet both technical requirements and business objectives.

Best practices include:

  • Regular architectural reviews and assessments
  • Comprehensive monitoring and measurement systems
  • Clear documentation of architectural decisions and their rationale
  • Continuous feedback loops from users and stakeholders

As software systems continue to evolve, the importance of architectural characteristics will only increase. Teams must stay informed about emerging trends and technologies while maintaining focus on fundamental architectural principles.

CoStrategix helps companies build new software products or assess existing platforms. If your company is planning an acquisition or has recently merged with another company and you’re looking to integrate multiple systems, we can help evaluate and make recommendations for moving forward.

CoStrategix is a strategic technology consulting and implementation company that bridges the gap between technology and business teams to build value with digital and data solutions. If you are looking for guidance on data management strategies and how to mature your data analytics capabilities, we can help you leverage best practices to enhance the value of your data. Get in touch!