The GNU ARM Embedded Toolchain provides a robust set of tools for developing, testing, and debugging ARM Cortex-M0/M1 based applications. One of the key features it offers is the ability to perform comprehensive code safety assessments using the GNU-ARM tools included in the toolchain.
Code safety refers to detecting potential bugs, errors or vulnerabilities in the software that could lead to unexpected or unintended behavior at runtime. This is critical for Cortex-M0/M1 devices which are often used in deeply embedded systems where reliability and real-time determinism are paramount.
Static Analysis with GCC and GAS
The GNU ARM Compiler (GCC) included with the GNU-ARM toolchain provides powerful static analysis capabilities to identify problems with code safety. Using GCC with the “-Wall” and “-Wextra” flags will enable a wide range of useful warnings that can point out issues like:
- Potential null pointer dereferences
- Uninitialized variables
- Unused variables/functions
- Type conversion mismatches
- Buffer overflows
GCC’s static analysis will inspect the code as it is compiled and alert the developer of any suspicious code constructs. This allows problems to be fixed early before they manifest. Additional warning flags like “-Wstack-usage” and “-Wunsafe-loop-optimizations” can enable even more rigorous checking.
The GNU Assembler (GAS) will also perform safety checks on the generated assembly code, warning about issues like:
- Misaligned memory accesses
- Unaligned branch targets
- Undefined opcodes
- Debugging information mismatches
Fixing assembly code issues uncovered by GAS will ensure the final executable matches intended behavior and specifications.
Dynamic Analysis with Valgrind
While static analysis examines code without running it, dynamic analysis looks at runtime behavior by executing either on real hardware or a simulator. The GNU-ARM toolchain includes Valgrind, a dynamic analysis tool that can detect problems like:
- Invalid memory accesses
- Memory leaks
- Uninitialized value use
- Invalid lock use
Valgrind instruments the generated code before execution to monitor all memory accesses, branches, and machine state changes. This provides visibility into the actual run-time behavior of an application to identify deviations from expectations.
For example, Valgrind can identify uses of uninitialized local variables, which can cause unintended behavior that may be difficult to discover through static analysis alone. It gives a more complete assessment of code safety across potential execution paths.
Stack Usage Analysis
Since Cortex-M0/M1 devices have relatively small memory sizes, it is important to ensure stack usage stays within expected bounds. Exhaustion of available stack space can lead to catastrophic crashes due to corruption of static data. GNU-ARM’s stack usage analyzer can detect such potential overflows.
To perform stack analysis, the application is first compiled with GCC’s “-fstack-usage” flag. This embeds static stack usage information within the executable. The GNU-ARM “arm-none-eabi-stack” tool can then analyze this stack usage data to check that:
- Overall stack usage is below total available stack size
- Peak stack usage by each function stays in expected bounds
The tool will report any stack overflows identified and the offending functions involved. The developer can then optimize the code to reduce stack requirements and prevent crashes.
Memory Usage Analysis
In addition to stack usage, analyzing overall memory consumption is important for Cortex-M0/M1’s constrained resources. The GNU-ARM tools provide a memory usage analyzer to breakdown total usage by application section as well as peak consumption.
To perform memory analysis, the application is compiled with “-fmem-report” to embed memory usage data in the executable. The “arm-none-eabi-size” tool included with GNU-ARM can then report statistics like:
- Memory usage by code, data, stack, heap sections
- Peak memory consumed during execution
- Fragmentation analysis
- Call stack for peak consumption points
This pinpoints areas of the application that may be consuming excessive memory. The developer can then optimize the code to reduce its memory footprint if needed.
Sanitizers for Run-Time Checks
GCC and Clang within the GNU-ARM toolchain also provide “sanitizers” that can instrument code to perform run-time verification of memory safety and thread safety.
Sanitizers available include:
- AddressSanitizer – Detects memory access issues like buffer overflows or use-after-free bugs
- ThreadSanitizer – Finds data races caused by improper synchronization between threads
- MemorySanitizer – Checks for uninitialized reads that could contain invalid data
- UndefinedBehaviorSanitizer – Catches undefined behavior like null pointer dereferences
Although sanitizers incur runtime overhead, they provide an automated means of augmenting dynamic analysis during development. They serve to reinforce the verification performed by static analysis.
For example, enabling AddressSanitizer during testing could identify buffer overflows that static analysis missed. Runtime sanitization helps catch any residual code safety issues before software release.
Fuzz Testing for Robustness
In addition to directed testing, fuzz testing can evaluategenerate random invalid, unexpected, or malicious test cases. This reveals weaknesses where software may crash, timeout, or behave incorrectly given unusual inputs.
For ARM Cortex-M devices, fuzzing tools like AFL can be used to rigorously exercise interface code and uncover edge cases that could compromise safety. Starting with valid inputs, AFL mutates data in guided iterations to maximize code coverage and incidents found.
Fuzzing tools integrate well with sanitizers like AddressSanitizer to detect any memory corruption issues triggered during testing. This provides automated resilience testing and an additional line of defense for code safety.
The comprehensive static and dynamic analysis capabilities provided by the GNU-ARM toolchain enable thorough verification of code safety for ARM Cortex-M0/M1 applications. Automated checks during compilation, at runtime, and via intelligent fuzzing provide broad coverage of potential issues.
Leveraging these facilities gives developers high confidence in software reliability for even deeply embedded Cortex-M deployments where code quality is paramount. The GNU-ARM toolchain offers a reliable path to robust and safe firmware for resource constrained devices.