I-SEE: Managing the Complexity of Modern Software Ecosystems
The relentless rise in software complexity has transformed the technological landscape, with systems growing not only more intricate but also increasingly interconnected across software, hardware, and human interactions. What began as experimental, almost playful technological endeavors have become the backbone of critical infrastructure, deeply interwoven with technologies and processes such as power grids, financial services, communication networks, and production systems.
These distributed software systems form the digital nervous system of modern society, creating systemic necessities with circular dependencies spanning multiple domains. These dynamic environments produce emergent phenomena and behaviors that are not easily understood through classical processes. Complex software systems today resemble biological ecosystems more than conventional technical systems. These software ecosystems are inherently dynamic, driven by feedback loops across various levels, rendering static analyses and outdated metaphors inadequate.
To ensure software quality, long-term maintainability, and efficiency, an external perspective, such as that provided by tools like Drvless, is essential. But an additional internal view into the dynamics and support for managing the evolution of software is indispensable. Without a deep understanding of these dynamics, interventions risk being too late or incomplete, failing to address the root causes of issues in these interconnected systems.
To tackle this challenge, Objentis, in collaboration with our research partner SBA Research, introduces I-SEE (Integrated Software Ecosystem Evaluation), a pioneering research project and product designed to manage the complexities of modern software ecosystems.
I-SEE analyzes software ecosystems in their evolution: code ages, teams change, and dependencies evolve. Using comprehensive metrics and machine learning, I-SEE evaluates these dynamics from technical, social, and strategic perspectives to proactively manage quality and risks.
I-SEE offers:
- Predictive Maintenance: AI-supported pattern recognition of code, team, and change metrics identifies maintenance-intensive areas early.
- Cognitive & Social Factors: Analysis of code comprehensibility, team knowledge, and knowledge distribution.
- External Dependencies: Management and risk assessment of dependencies and their relevance.
We provide a
- Developers: Early feedback on problematic code changes
- QA & Test: Prioritization of test strategies based on identified high-risk areas.
- Engineering Leads: Insights into workload distribution and knowledge and quality risks over the software landscape
- Management & Compliance: Due diligence, audit support, and risk assessment
I-SEE supports a range of stakeholders in your organisation:
- Developers: Early feedback on problematic code changes
- QA & Test: Prioritization of test strategies based on identified high-risk areas.
- Engineering Leads: Insights into workload distribution and knowledge and quality risks over the software landscape
- Management & Compliance: Due diligence, audit support, and risk assessment