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What are the best practices for debugging software code that involves edge computing or fog computing?

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Debugging software code can be challenging, especially when it involves edge computing or fog computing. These are paradigms that distribute computation and data processing across multiple nodes, such as sensors, gateways, and cloud servers, to achieve low latency, high scalability, and efficient resource utilization. However, they also introduce complexity and heterogeneity that can make debugging more difficult. In this article, we will explore some of the best practices for debugging software code that involves edge computing or fog computing.

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LEI WANG

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1 Use a consistent logging strategy

Logging is a vital tool for debugging software code, as it helps track the flow of code, identify errors, and analyze performance. However, logging can be difficult when dealing with multiple nodes, different platforms, and various communication protocols. To ensure success, use a consistent logging strategy that follows these principles: use a common format and level for log messages (e.g. JSON or XML) and define what constitutes a debug, info, warning, error, or critical message; employ a centralized logging system (e.g. ELK stack or EFK stack) to collect and store logs from all nodes; add a correlation identifier to link related log messages across nodes (e.g. request ID or session ID); include a timestamp to synchronize log messages across nodes (e.g. UTC or common clock source); and provide context for the log message (e.g. node name, function name, input parameters).

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LEI WANG

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Using the right tools for different tasks: 1. python frontend - cpp backend project: make sure your project has attached debug info with source codes, then jump from python frontend to cpp backend with gdb, lldb to see what was going on. 2. linked to the right version of libraries: using ldd to make sure the right version libraries are linked against. Best practices include using combination of docker, conda to manage development environment to make sure right softwares are used. Dependency manager should be always shipped with the software: Cmake manager (catkin, for example, using git, apt-get, yum scripts to manage is also accepted practice. Recommend to develop your own cmake dep manager to manage) pip reqirements.txt dockerfile

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2 Apply automated testing and monitoring

Testing and monitoring are essential tools for debugging software code, as they can help detect and prevent bugs, ensure quality standards, and optimize resource consumption. However, when dealing with dynamic and distributed environments like edge computing or fog computing, testing and monitoring can be challenging. Therefore, automated testing and monitoring should be applied that follows certain principles. For example, using a test-driven development (TDD) or behavior-driven development (BDD) approach to define and execute test cases before and after writing your code. Additionally, a continuous integration/continuous delivery (CI/CD) pipeline should be utilized to automate the building, testing, and deploying of your code. Furthermore, a distributed tracing system should be employed to track and visualize the execution of your code across nodes while a performance testing and benchmarking tool can simulate and measure the workload and throughput of your code. Lastly, a health check and alerting system should be used to monitor and notify the status and metrics of your code.

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LEI WANG

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3. unit test + coverage gtest, boot-unitest-framekwork, coverage tool should be always used for nightly build to make sure errors are not accumulated to isolate problems you are facing. 4. sanitizer tool + perf tool perf tool may not be directly related to the code you are debugging, but it helps for out to find out something unusual, e.g. memory leak code snippet. 5. Make your multi-threads program configurable: whenever you face problem in your multi-thread program, make sure can convert your program a single thread program or a simple 1xproduce-1xconsumer case, so that debug is easy.

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3 Adopt a modular and scalable design

Design is a crucial tool for debugging software code, as it helps to simplify and clarify the code, improve maintainability and reusability, and enhance user experience. However, when dealing with diverse and changing requirements, such as edge computing or fog computing, design can be challenging. Therefore, you should adopt a modular and scalable design that follows specific principles. For example, using a microservice architecture to decompose the code into small and independent units that communicate through well-defined interfaces (such as REST, gRPC, or MQTT). Additionally, you should consider using a containerization and orchestration platform to package, deploy, and manage your code across nodes (such as Docker, Kubernetes, or Swarm). Furthermore, you should use a serverless framework to abstract and automate the provisioning and execution of your code (such as AWS Lambda, Google Cloud Functions, or Azure Functions). In addition to this, you should use a configuration management and version control system to store and track the changes and dependencies of your code (such as Ansible, Puppet, or Git). Lastly, you should use a code quality and security tool to analyze and enforce the standards and best practices of your code (such as SonarQube, CodeClimate, or Snyk).

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for debugging software code that involves edge computing or fog computing?

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4

1 Use a consistent logging strategy

2 Apply automated testing and monitoring

3 Adopt a modular and scalable design

4 Here’s what else to consider

System Development

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What are the best practices for debugging software code that involves edge computing or fog computing?

1

2

3

4

1 Use a consistent logging strategy

2 Apply automated testing and monitoring

3 Adopt a modular and scalable design

4 Here’s what else to consider

System Development

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