Debugging and Profiling Software
Debugging and profiling software forms the backbone of embedded systems development, providing engineers with the visibility needed to identify bugs, optimize performance, and ensure code quality. These tools bridge the gap between writing code and deploying reliable embedded applications, offering capabilities that range from stepping through individual instructions to analyzing system-wide performance characteristics over extended periods.
The complexity of modern embedded systems demands sophisticated analysis tools. Microcontrollers running at hundreds of megahertz, managing multiple peripherals, and responding to real-time events present debugging challenges that traditional print-statement debugging cannot address. Professional debugging and profiling software provides non-intrusive observation of system behavior, helping developers understand what their code is actually doing rather than what they intended it to do.
This comprehensive guide explores the essential categories of debugging and profiling software used in embedded systems development, from foundational tools like GDB and OpenOCD through specialized analyzers for memory, performance, power consumption, and real-time behavior. Understanding these tools and their appropriate applications enables developers to build more reliable, efficient, and maintainable embedded systems.
GDB and OpenOCD
The GNU Debugger (GDB) combined with OpenOCD (Open On-Chip Debugger) forms the foundation of open-source embedded debugging. This powerful combination provides professional-grade debugging capabilities without licensing costs, making sophisticated debugging accessible to developers working on projects of all sizes from hobbyist experiments to commercial products.
Understanding GDB Architecture
GDB operates as a source-level debugger that understands the relationship between compiled machine code and the original source files. When debugging embedded systems, GDB typically runs on a host computer and communicates with the target hardware through a debug probe. This remote debugging architecture allows developers to use the full power of their development workstation while controlling code execution on resource-constrained embedded targets.
The debugger provides essential capabilities including breakpoints that halt execution at specific code locations, watchpoints that trigger when memory locations change, and single-stepping that advances execution one instruction or source line at a time. GDB can examine and modify memory, inspect register contents, evaluate expressions in the context of the running program, and navigate the call stack to understand how the current execution point was reached.
OpenOCD as Debug Interface
OpenOCD serves as the bridge between GDB and the target hardware, translating GDB's remote serial protocol commands into the specific debug interface protocols required by different microcontroller architectures. OpenOCD supports a wide range of debug adapters including JTAG and SWD probes from various manufacturers, enabling a single debugging workflow across diverse hardware platforms.
Configuration of OpenOCD involves specifying both the debug adapter being used and the target microcontroller. OpenOCD maintains an extensive library of configuration scripts for common hardware combinations, though custom configurations are sometimes necessary for unusual hardware designs. The tool handles the low-level details of debug port initialization, flash programming, and maintaining the debug connection during development sessions.
Advanced GDB Features
Beyond basic debugging, GDB offers advanced features particularly valuable for embedded development. Conditional breakpoints trigger only when specified conditions are met, reducing the tedium of breaking on frequently executed code while waiting for a rare condition. Reverse debugging on supported platforms allows stepping backward through execution history, invaluable for understanding how a bug was reached.
GDB's scripting capabilities enable automation of complex debugging workflows. Python integration allows developers to create custom commands, automate repetitive debugging tasks, and build specialized analysis tools. These capabilities prove particularly valuable when debugging complex bugs that require extensive data collection or when testing requires automated verification of system behavior.
Integrated Development Environment Support
While GDB can be used from the command line, most developers access its capabilities through integrated development environments that provide graphical interfaces. Eclipse-based IDEs, Visual Studio Code with appropriate extensions, and vendor-specific development environments typically use GDB as their underlying debug engine while providing visual displays of source code, variables, memory, and registers.
These graphical interfaces make debugging more accessible without sacrificing the power of the underlying GDB engine. Developers can click to set breakpoints, hover over variables to see values, and navigate call stacks visually while still having access to the GDB command line for advanced operations.
Static Code Analyzers
Static code analyzers examine source code without executing it, identifying potential bugs, security vulnerabilities, and code quality issues before the code ever runs on hardware. These tools complement runtime debugging by catching entire categories of problems during development rather than after deployment, when fixes are far more expensive.
Types of Static Analysis
Static analyzers perform various types of analysis depending on their sophistication and focus. Basic syntax and style checkers enforce coding standards and catch simple errors that compilers might miss or only warn about. More advanced tools perform data flow analysis to track how values propagate through code, identifying potential null pointer dereferences, uninitialized variable usage, and buffer overflows.
Abstract interpretation techniques allow some analyzers to reason about all possible execution paths through code, identifying problems that might only occur under specific runtime conditions. These tools can detect issues like integer overflows, race conditions in concurrent code, and violations of API contracts that would be difficult to find through testing alone.
Popular Static Analysis Tools
The embedded development ecosystem includes numerous static analysis options ranging from free open-source tools to commercial products with extensive analysis capabilities. PC-lint and its successors have long served the embedded community, enforcing MISRA C guidelines and catching common embedded programming errors. Coverity and Polyspace provide deep analysis capabilities used in safety-critical industries where code quality requirements are stringent.
Open-source options include Cppcheck for C and C++ analysis, the Clang Static Analyzer integrated with the LLVM compiler infrastructure, and various linting tools that focus on specific aspects of code quality. Many development teams combine multiple tools to achieve comprehensive coverage, as different analyzers have different strengths in detecting various types of issues.
Integration into Development Workflows
Maximum benefit from static analysis comes from integrating these tools into daily development workflows rather than running them occasionally. Continuous integration systems can run static analysis on every code commit, catching issues before they enter the main codebase. IDE integration provides immediate feedback as developers write code, making it easy to fix issues while the code is still fresh in mind.
Configuring static analyzers requires balancing thoroughness against noise. Overly aggressive settings that generate many false positives lead developers to ignore warnings, defeating the tool's purpose. Effective configuration involves suppressing warnings for code patterns that are intentional in the specific project while maintaining sensitivity to genuine issues.
MISRA Compliance
The Motor Industry Software Reliability Association (MISRA) guidelines define coding standards specifically designed for safety-critical embedded systems. MISRA C and MISRA C++ restrict language usage to subsets that avoid constructs prone to errors or undefined behavior. Static analyzers that support MISRA compliance checking help development teams adhere to these guidelines, which are often required in automotive, aerospace, and medical device development.
Achieving MISRA compliance involves not just running analysis tools but understanding the rationale behind each rule and making informed decisions about necessary deviations. Documentation of deviation justifications forms part of the compliance process, demonstrating that apparent violations were considered and determined acceptable for specific reasons.
Memory Leak Detectors
Memory management errors represent a significant source of bugs in embedded systems, particularly in C and C++ programs that require manual memory management. Memory leak detectors identify problems including memory leaks where allocated memory is never freed, use-after-free errors where deallocated memory is accessed, buffer overflows that write beyond allocated boundaries, and double-free errors that attempt to deallocate the same memory twice.
Dynamic Memory Analysis Tools
Valgrind stands as one of the most comprehensive memory analysis tools available, though its heavyweight instrumentation approach makes it more suitable for hosted development environments than resource-constrained embedded targets. Valgrind's Memcheck tool tracks every memory allocation and access, detecting errors with high precision while providing detailed information about where problems originated.
AddressSanitizer (ASan) represents a newer approach that inserts instrumentation at compile time rather than runtime. This approach offers lower overhead than Valgrind while still detecting many memory errors. ASan integration into GCC and Clang compilers makes it accessible for embedded development when running code on development hosts or sufficiently capable embedded targets.
Embedded-Specific Considerations
Many embedded systems avoid dynamic memory allocation entirely after initialization to eliminate memory management bugs and ensure deterministic behavior. However, systems that do use dynamic allocation during operation face unique challenges. Limited memory means leaks cause problems much faster than on desktop systems, while real-time constraints may preclude the overhead of instrumented memory checking.
Lightweight memory debugging approaches designed for embedded systems include custom allocators that track allocations with minimal overhead, canary values placed around allocations to detect overflows, and periodic heap walking to identify leaked blocks. These techniques provide less comprehensive checking than heavyweight tools but can operate within embedded resource constraints.
Stack Analysis
Stack overflow represents a particularly dangerous memory error in embedded systems, potentially corrupting program data and causing erratic behavior difficult to diagnose. Static analysis tools can estimate maximum stack usage by analyzing call graphs and local variable sizes, though recursion and function pointers complicate this analysis.
Runtime stack monitoring techniques include filling the stack with known patterns during initialization and periodically checking how much of this pattern remains untouched to determine high-water marks. Hardware memory protection units (MPUs) can trigger exceptions when stack boundaries are violated, providing immediate detection of stack overflows rather than silent corruption.
Heap Fragmentation Analysis
Even without leaks, dynamic memory systems can suffer from fragmentation where available memory becomes scattered in chunks too small to satisfy allocation requests. Specialized tools analyze heap state over time, visualizing how allocations and deallocations affect memory availability. Understanding fragmentation patterns helps developers design allocation strategies that maintain usable free memory throughout system operation.
Code Coverage Tools
Code coverage tools measure which portions of source code execute during testing, providing quantitative data about test effectiveness. Coverage metrics help identify untested code that might harbor bugs and guide test development efforts toward areas needing additional attention. While high coverage does not guarantee correctness, low coverage definitively indicates inadequate testing.
Coverage Metrics
Various coverage metrics measure different aspects of test completeness. Statement coverage tracks which source statements execute, providing a basic measure of code exercised by tests. Branch coverage extends this to track whether each branch direction (true and false) of conditional statements has been taken. Condition coverage examines individual Boolean subexpressions within complex conditions, ensuring each has been evaluated to both true and false.
Modified Condition/Decision Coverage (MC/DC) represents the most stringent coverage criterion commonly required in safety-critical development. MC/DC requires demonstrating that each condition in a decision independently affects the outcome, providing strong assurance that all aspects of complex Boolean logic have been tested. Achieving MC/DC coverage requires carefully designed test cases that isolate the effect of each condition.
Instrumentation Approaches
Coverage tools employ various instrumentation strategies to track execution. Compiler-based instrumentation, such as that provided by GCC's gcov or LLVM's source-based coverage, inserts counters during compilation. This approach is efficient and accurate but requires recompilation with coverage enabled, potentially affecting timing-sensitive behavior.
Hardware-assisted coverage uses processor trace capabilities to record execution without modifying the code. This approach preserves timing behavior but requires specific hardware support and appropriate debug probes. Some coverage tools combine both approaches, using hardware tracing where available and falling back to instrumentation otherwise.
Coverage in Embedded Development
Embedded systems present unique coverage challenges. Code that runs only in response to specific hardware events or error conditions may be difficult to exercise in testing. Interrupt service routines, exception handlers, and defensive code for unlikely hardware failures often show low coverage despite being critical for system reliability.
Testing strategies for embedded coverage include hardware-in-the-loop testing that exercises code with real peripherals, simulation environments that can inject unusual conditions, and fault injection that deliberately triggers error handling paths. Achieving high coverage in embedded systems typically requires combining multiple testing approaches rather than relying solely on functional testing.
Continuous Integration and Coverage Tracking
Integrating coverage measurement into continuous integration provides ongoing visibility into test effectiveness. Coverage reports generated with each build reveal whether changes are adequately tested and whether overall coverage is improving or degrading. Coverage gates that reject builds falling below threshold values help maintain testing standards.
Historical coverage trending identifies areas of the codebase that consistently resist testing, suggesting either difficult-to-test designs that might benefit from refactoring or missing test infrastructure. Regular coverage review ensures testing investment focuses on areas providing the greatest risk reduction.
Performance Profilers
Performance profilers measure where programs spend execution time, revealing optimization opportunities and helping developers understand actual rather than assumed behavior. Profiling is essential for meeting real-time requirements, maximizing battery life in portable devices, and ensuring applications remain responsive under varying loads.
Sampling vs. Instrumentation Profiling
Profilers employ two fundamental approaches to collecting timing data. Sampling profilers periodically interrupt execution and record the current program counter and call stack. Statistical analysis of many samples reveals where the program spends most time. Sampling has low overhead and does not distort timing behavior but provides statistical approximations rather than exact measurements.
Instrumentation profilers insert code at function entries and exits to record precise timing information. This approach provides exact call counts and timing but adds overhead that can significantly affect program behavior. The overhead is particularly problematic for small, frequently called functions where instrumentation time may exceed actual execution time.
Call Graph and Flat Profiles
Flat profiles list functions sorted by execution time, immediately revealing the hottest functions consuming the most CPU cycles. This simple view guides initial optimization efforts toward functions where improvements will have the greatest impact. However, flat profiles do not reveal calling context, potentially hiding optimization opportunities in callers of hot functions.
Call graph profiles maintain calling relationships, showing not just how much time each function consumes but how that time is distributed among callers. This context helps identify whether a function is inherently expensive or is being called excessively from specific call sites. Call graph analysis often reveals optimization opportunities that flat profiles would miss.
Hardware Performance Counters
Modern processors include hardware performance monitoring units that count events such as cache misses, branch mispredictions, and memory accesses. Profilers that access these counters provide insights beyond execution time, revealing whether performance issues stem from algorithmic inefficiency, cache behavior, or memory access patterns.
Event-based profiling samples not on time intervals but on hardware events, creating profiles weighted by cache misses, branch mispredictions, or other metrics. This approach directly reveals code responsible for specific performance problems rather than requiring developers to infer causes from timing data.
Embedded Profiling Considerations
Resource-constrained embedded systems often cannot accommodate the overhead of full profiling instrumentation. Lightweight profiling techniques include using hardware timers to measure specific code sections of interest, sampling with low frequency to minimize impact, and using trace capabilities that offload recording to external tools.
Profiling real-time systems requires care to avoid measurement artifacts. Profiling overhead that delays responses can trigger timing failures that would not occur in normal operation, leading to misleading conclusions. Techniques that minimize probe effect, such as hardware-assisted profiling, are particularly valuable for real-time system analysis.
Power Profiling Software
Power consumption is a critical design parameter for battery-powered devices, energy-harvesting systems, and any application where thermal management is constrained. Power profiling software correlates energy consumption with software behavior, helping developers optimize code for minimal power usage while maintaining required functionality.
Power Measurement Integration
Power profiling requires correlating power measurements with code execution. This correlation typically involves synchronizing data from external power analyzers with software execution traces. Some development boards include integrated power measurement capabilities that simplify this correlation, while standalone power analyzers require explicit synchronization through trigger signals or timestamps.
Measurement resolution affects what optimization opportunities can be identified. High-bandwidth power measurement captures brief current spikes from individual operations, revealing opportunities to optimize specific code sections. Lower-resolution measurement better suits system-level analysis of sleep modes and duty cycles where individual operations blend together.
Energy-Aware Profiling Tools
Several tool suites provide integrated power profiling capabilities. ARM's Energy Probe and associated software correlate power measurements with execution traces on ARM-based targets. Silicon vendor tools often include power analysis features optimized for their specific microcontroller families, accessing internal measurement points and providing accurate characterization of different operating modes.
Specialized power profiling tools from companies like Otii and Joulescope provide high-resolution current measurement with deep integration into development workflows. These tools capture current waveforms with microsecond resolution, enabling analysis of individual peripheral operations and precise characterization of sleep mode transitions.
Optimization Strategies Revealed by Power Profiling
Power profiling typically reveals opportunities in several categories. Sleep mode utilization analysis shows whether the system achieves low-power states when inactive and how quickly it returns to sleep after processing. Peripheral power management examination reveals whether unused peripherals are properly disabled. Processing efficiency analysis identifies whether algorithms complete quickly enough to maximize sleep time.
Power profiling often reveals surprising results that contradict intuition. Faster execution that enables longer sleep periods may consume less total energy than slower, seemingly more efficient code. Peripheral access patterns that seem efficient may cause unnecessary clock domain activity. Data-driven optimization based on actual measurements consistently outperforms intuition-based approaches.
Battery Life Estimation
Beyond identifying optimization opportunities, power profiling enables accurate battery life estimation. By characterizing power consumption across operating modes and understanding duty cycles, developers can predict how long devices will operate on specific batteries. This capability is essential for product planning and helps identify when power optimization is necessary versus merely desirable.
Real-Time Trace Tools
Real-time trace tools capture detailed execution history without stopping the processor, enabling analysis of timing-critical behavior that conventional debugging would disturb. Trace capabilities are essential for diagnosing issues that occur only under specific timing conditions or that disappear when breakpoints are inserted.
Trace Technology Overview
Trace systems record processor activity to memory or external capture devices as the processor executes at full speed. ARM's Embedded Trace Macrocell (ETM) and CoreSight architecture provide trace capabilities on ARM processors, while other architectures have comparable features. These hardware blocks compress trace data for efficient capture while maintaining enough information to reconstruct execution flow.
Trace data can include program counter values showing execution path, data addresses for memory accesses, timestamps for timing analysis, and operating system context switches. The level of detail captured depends on available trace bandwidth and storage, with developers selecting what information is most relevant for their debugging needs.
Trace Capture Methods
On-chip trace buffers provide limited storage within the microcontroller itself. When the buffer fills, oldest data is overwritten, preserving recent execution history. This approach captures events leading up to a trigger condition, such as an error or specific code location, without requiring external hardware.
Off-chip trace ports stream trace data to external capture hardware, enabling continuous capture of extended execution periods. High-speed trace ports can sustain bandwidth needed for detailed trace without data loss, though they require appropriate debug probes and may consume pins needed for other purposes in production.
Trace Analysis Capabilities
Trace analysis software reconstructs execution history from captured trace data, presenting information in various views useful for different analysis tasks. Execution flow views show the sequence of function calls and branches taken. Timeline views correlate execution with time, revealing when specific code executed relative to external events. Statistical views summarize execution patterns over trace periods.
Advanced analysis features include detecting execution of specific code patterns, measuring interrupt latencies, and identifying unusual execution sequences. Trace-based code coverage provides coverage data without instrumentation overhead, valuable for real-time systems where instrumentation would affect timing behavior.
Real-Time Operating System Awareness
When debugging systems running real-time operating systems (RTOS), trace tools can provide OS-aware analysis. Context switch tracking shows when and why task switches occurred. Ready and blocked state transitions reveal scheduling behavior. Priority inversion detection identifies scheduling anomalies that could affect system timing.
RTOS vendors often provide plugins for popular trace tools that decode OS-specific trace data. This integration enables analysis of complex multi-threaded behavior that would be nearly impossible to understand through breakpoint-based debugging alone.
Protocol Analyzers in Software
Software-based protocol analyzers decode and display communication between embedded devices and peripherals, helping developers understand and debug interface behavior. While hardware protocol analyzers capture raw signals, software analyzers interpret captured data or instrument communication stacks to provide higher-level protocol views.
Serial Protocol Analysis
Serial communication protocols including UART, SPI, and I2C are fundamental to embedded systems. Software analyzers can monitor these interfaces through logic analyzers or debug probes that capture raw data, then decode the captured bits into meaningful protocol transactions. Open-source tools like sigrok provide extensible frameworks for adding protocol decoders.
Beyond simple decoding, analyzers can detect protocol errors, measure timing parameters, and identify violations of protocol specifications. Timing analysis reveals whether systems meet setup and hold requirements, while transaction analysis verifies correct command and response sequences.
Network Protocol Analysis
Embedded systems increasingly incorporate network connectivity, making network protocol analysis essential. Tools like Wireshark capture and analyze network traffic at multiple protocol layers, from Ethernet frames through application-layer protocols. Embedded developers use these tools to debug TCP/IP stacks, verify protocol implementations, and diagnose connectivity issues.
For resource-constrained devices that cannot run full network analyzers, developers typically capture traffic using external monitoring hardware or network switches with port mirroring capabilities. Analysis then occurs on a development workstation with full analysis capabilities.
Wireless Protocol Analysis
Wireless protocols including Bluetooth, WiFi, Zigbee, and LoRa present unique analysis challenges since over-the-air traffic cannot be passively monitored with simple hardware. Specialized sniffers capture wireless traffic for analysis by software tools. Protocol stacks for these technologies often include built-in logging that provides insight into higher-layer behavior without air interface capture.
Vendor tools for wireless protocol analysis typically provide the most complete analysis capabilities for specific protocols, understanding proprietary extensions and implementation details. Open-source alternatives may provide sufficient capability for standard protocol features while lacking support for vendor-specific aspects.
Integration and Workflow Considerations
Effective use of debugging and profiling tools requires integrating them into development workflows rather than treating them as occasional diagnostic aids. Consistent use throughout development catches issues early when they are easiest to fix, while establishing baseline measurements enables detection of performance regressions.
Tool Selection and Standardization
Development teams benefit from standardizing on common toolsets that all team members understand and can use effectively. This standardization accelerates onboarding of new team members and ensures debugging artifacts can be shared and understood across the team. Tool selection should consider factors including target hardware support, integration with existing development environments, and licensing costs.
While standardization provides benefits, flexibility to use specialized tools for specific problems remains important. A standard toolchain for daily development work can coexist with specialized tools brought in when specific issues require their capabilities.
Continuous Integration Integration
Automated execution of analysis tools in continuous integration systems provides consistent checking without requiring manual execution. Static analyzers, memory checkers, and code coverage measurement all benefit from CI integration. Automated analysis catches issues before they reach code review or production, reducing debugging effort later in development.
CI-generated reports provide historical tracking of code quality metrics. Coverage trends, static analysis warning counts, and other metrics reveal whether quality is improving or degrading over time. Visibility into these trends helps teams prioritize quality improvement efforts.
Documentation and Knowledge Sharing
Debugging complex issues often produces insights valuable beyond the immediate problem. Documenting debugging sessions, analysis approaches, and root causes builds institutional knowledge that accelerates future debugging efforts. This documentation is particularly valuable for issues that recur or for onboarding team members who will maintain the codebase.
Sharing profiling data and analysis results helps teams develop shared understanding of system behavior. Performance baselines, power consumption profiles, and timing characterizations serve as references for evaluating changes and detecting regressions.
Best Practices for Debugging and Profiling
Effective debugging and profiling requires both appropriate tools and disciplined approaches to using them. Following established best practices helps developers extract maximum value from their tools while avoiding common pitfalls that can waste time or produce misleading results.
Systematic Debugging Approach
Methodical debugging consistently outperforms random experimentation. Starting with clear problem definition, forming hypotheses about potential causes, designing experiments to test hypotheses, and carefully interpreting results leads to faster root cause identification. Documenting the debugging process helps maintain focus and provides a record for future reference.
Reproducing problems reliably before attempting to fix them ensures that fixes can be verified and reduces the risk of masking symptoms rather than addressing root causes. When problems resist reproduction, systematic variation of conditions helps identify triggering factors.
Profiling Before Optimizing
Optimization without profiling data typically focuses on wrong areas, consuming development effort without improving performance. Profiling first identifies actual bottlenecks, directing optimization effort where it will have greatest impact. The common developer intuition about performance hotspots is frequently wrong, making measurement essential.
Profiling should occur with realistic workloads and data. Synthetic test cases may not exercise code paths that dominate real-world execution. Where possible, profiling production or production-like scenarios provides the most actionable data.
Avoiding Measurement Artifacts
All debugging and profiling tools affect system behavior to some degree. Understanding and minimizing these effects ensures that observations reflect actual system behavior rather than artifacts of observation. Using the least intrusive measurement technique that provides needed information reduces probe effect.
When intrusive measurement is unavoidable, understanding how it affects results enables appropriate interpretation. Instrumentation overhead should be considered when interpreting timing data. Debug builds with reduced optimization may execute differently than release builds, potentially masking or revealing issues.
Maintaining Tool Proficiency
Debugging and profiling tools are only useful when developers know how to use them effectively. Regular practice with tools, including during routine development rather than only during crises, builds proficiency. Exploring advanced features during non-urgent situations prepares developers to use them effectively when needed.
Staying current with tool updates and new capabilities ensures access to improved analysis features. Tool vendors regularly add capabilities that can significantly improve debugging efficiency, but these capabilities only help developers who learn about and adopt them.
Conclusion
Debugging and profiling software provides essential visibility into embedded system behavior, enabling developers to identify and fix bugs, optimize performance, and ensure code quality. From foundational tools like GDB and OpenOCD that provide interactive debugging capabilities through specialized analyzers for memory, code coverage, performance, power consumption, and real-time behavior, these tools address the diverse challenges of embedded software development.
Static analyzers catch potential bugs before code executes, reducing the cost of defect repair by identifying issues early in development. Memory analyzers detect the allocation errors and leaks that plague systems with manual memory management. Code coverage tools measure test effectiveness, guiding testing efforts toward untested code. Performance profilers reveal where optimization will have greatest impact, while power profilers guide the energy optimization critical for battery-powered devices.
Real-time trace tools capture execution history without disturbing timing-critical behavior, enabling analysis of issues that conventional debugging cannot address. Protocol analyzers decode communication between devices, simplifying interface debugging. Together, these tools provide comprehensive visibility into all aspects of embedded system behavior.
Effective use of these tools requires integration into development workflows, appropriate tool selection for target hardware and development needs, and disciplined debugging and profiling practices. Teams that invest in tool proficiency and consistent use throughout development build more reliable embedded systems with less debugging effort. As embedded systems grow more complex, sophisticated debugging and profiling capabilities become not optional conveniences but essential elements of professional development practice.