Travancore Analytics

Best 5 Strategies for Managing Complex Code in Embedded System Development

May 16th, 2024

Category: embedded systems

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Posted by: Team TA

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Embedded systems, combining hardware and software, have long played a vital role across the aerospace, automotive, machinery, and electronics industries. In automotive sectors, embedded software powers everything from anti-lock braking systems to advanced driver-assistance systems, contributing to a multibillion-dollar market with a projected 9% growth rate by 2030. The automotive sector provides a compelling example of the growing complexity in embedded systems, particularly evident in complex code in embedded system development. Similarly, embedded systems coordinate the interaction between manned and unmanned aircraft in aerospace, enhancing safety in critical situations. Even in the machinery industry, there’s a shift towards self-optimizing smart tools driven by embedded software. Technically, embedded systems integrate software, hardware, sensors, and actuators into cohesive units, forming active signal processing and control chains.

Understanding Complex Code in Embedded Systems

According to a recent report by McKinsey, embedded systems are becoming more complex due to various factors such as the need to comply with safety and security regulations, increased automation, rising connectivity, and achieving software/hardware compatibility. These complexities arise due to the demands of industry regulations, the use of machine learning technologies, cybersecurity concerns, and the synchronization of hardware and software development. Additionally, resource constraints such as limited energy, space, and cost add to the complexity. All these factors require careful management to ensure these systems’ successful development and operation.

Over the past decade, the complexity within the automotive sector has surged by 300%. There are approximately 100 control units in the average car, which contain thousands of software components and facilitate thousands of signal exchanges between subsystems. Each of these elements demands meticulous attention throughout the design, development, integration, testing, and validation processes.

Strategies for Managing Code Complexity in Embedded System Development

While there’s no one-size-fits-all solution for complexity management, there are effective strategies and techniques that can be tailored to specific situations. Five key steps have consistently proven their effectiveness in embarking on this journey:


Begin by segmenting your R&D portfolio into projects based on their complexity, considering both environmental and system complexities. This segmentation allows for a clearer understanding of the challenges and resource requirements associated with each project. Initial positioning within the matrix can be guided by insights from development leaders, but a comprehensive diagnosis requires a systematic analysis of complexity dimensions, including environmental complexity, system complexity (encompassing system design and Operational Design Domain), and the organization’s ability to handle complexity.

Within the subset of high-complexity projects, prioritize those critical to business impact or timeline objectives. However, exercise caution not to overlook high-value yet early-stage projects, as the opportunity to mitigate unnecessary complexity in these projects often diminishes rapidly. By strategically managing your R&D portfolio, you can optimize resource allocation and increase the likelihood of project success.


Understanding one’s position in the complexity management matrix begins with defining the scope of consideration, whether it’s a single embedded-system-development service project or a portfolio of projects. Initial positioning within the matrix can be guided by insights from development leaders, but a comprehensive diagnosis requires a systematic analysis of complexity dimensions, including environmental complexity, system complexity (encompassing system design and Operational Design Domain), and the organization’s ability to handle complexity. 

When dealing with crucial projects, it’s essential to conduct an honest analysis to determine their complexity level. This can be achieved by running a deep-dive assessment of the factors that drive system complexity and those that limit the ability to cope with it. It’s important to evaluate the impact of each driver against critical KPIs that determine the project’s success, such as life-cycle cost, time to market, competitiveness, and customer experience. 

It is crucial to identify key drivers within each dimension. These drivers include factors such as the breadth of corner cases in environmental complexity, the number of requirements and level of reuse in system complexity, and the level of toolchain integration and collaboration in coping with complexity. Once the primary source of complexity is identified—whether it’s system complexity, coping ability or a combination of both—appropriate strategies and levers can be selected to address the challenges effectively.


When there are critical projects at hand, it is important to choose the appropriate strategy for managing their complexity. You should prioritize the most effective strategies that have the highest chances of success based on the status of the project. Deep domain expertise can help prioritize requirements and reduce overall system complexity.

Dealing with complex code in embedded system can be difficult when developing large systems that require the involvement of multiple teams. Domain-driven design offers some useful concepts for managing complexity, such as using a ubiquitous language, bounded context, or model-driven design.

Standardizing tools, methods, and processes can also help improve an organization’s ability to deal with complexity. For example, a complex embedded system development team was able to improve its progress and speed by using a single tool for all tasks, which helped them quickly identify and resolve bottlenecks.


Once you’ve prioritized the levers, it’s time to put them into action. Engage all relevant departments, including R&D, product management, sales, procurement, and finance, to ensure swift alignment and drive meaningful change. It’s crucial to secure top-management support and involvement for efficient decision-making, particularly in reducing system complexity and enhancing the organization’s ability to handle complexity effectively. 

Implementing a model-driven engineering approach can revolutionize the verification and testing of embedded systems, leading to significant reductions in time, cost, and effort associated with their development. Embracing agile methodologies, such as Continuous Integration, within embedded system development empowers engineers to work swiftly while maintaining high quality. Through the adoption of DevOps principles and tools, teams can seamlessly scale their embedded development processes in sync with their agile development cycles, ensuring efficiency and agility throughout the development lifecycle.


To avoid repeating mistakes from the past, it is important to learn from successful cases of complexity management. This includes documenting valuable lessons that can be applied to future projects. To achieve lasting organizational change, it is crucial to create a sense of momentum towards complexity management. This can be done by making structural adjustments, enhancing operational models, implementing new tools, and increasing awareness of complexity. Ultimately, the goal is to embed complexity management into the organization’s ethos and apply tailored strategies across all areas of the business.


Achieving mastery in complexity management is achieved by using tools that help us connect different parts of a system, identify errors quickly and easily, and avoid having to do things manually. By managing complexity, companies can save money and invest it in new products and updates. This can help companies stay competitive and be more successful in the long run. Above all, inefficient complexity management is often caused by a lack of experienced people and domain expertise within the team. Experts in architecture and toolchain development should therefore be included in the teams since they may utilize their knowledge to resolve trade-off considerations. 

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