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This year’s boom in generative artificial intelligence has made embedded software developers reimagine what AI can do. Speculation surrounding AI’s ability to amplify developer productivity by two, according to a 2023 report McKinsey published on developer productivity, has sparked excitement and captured our imagination. Pioneers like Microsoft and Siemens lead the charge with groundbreaking generative AI breakthroughs and cutting-edge deployment architectures, driving the democratization of this transformative technology.
As teams increasingly harness the capabilities of generative AI tools, embedded developers must understand and navigate potential accuracy, confidentiality and intellectual property concerns.
Boosting efficiency
Generative AI, epitomized by the meteoric rise of ChatGPT and GitHub Copilot, has cemented its place as the fastest-growing technology in history. This breakthrough propels developers to unparalleled productivity. Instant code suggestions and recommendations streamline workflows, reducing manual labor and saving time. Additional benefits in embedded system development include more efficient debugging, clear code explanations and identification of clarity gaps. Overall, generative AI tools offer vital debugging and optimization support, promptly resolving errors and enhancing performance (see “7 expert tips for writing embedded software with ChatGPT”).
Moreover, generative AI accelerates rapid prototyping by generating code snippets at remarkable speed. This agility empowers developers to test functionalities and explore diverse implementation possibilities, fostering iterative design processes and fueling innovation. By harnessing the potential of generative AI tools, developers unlock their creative prowess and achieve unmatched productivity throughout the development lifecycle.
Addressing code accuracy challenges
Generative AI tools are adept but might lack full context and real-world limitations. Prioritizing code accuracy is crucial when using them. This includes meeting functional and performance requirements, compiler compatibility, hardware suitability and safety measures, as examples.
Testing and quality assurance are necessary to mitigate the risk of code “hallucinations,” and manual intervention is essential to verify and align the generated code with specific requirements, guaranteeing precision and reliability. This meticulous human oversight ensures that generative AI systems produce code that meets the highest standards of accuracy.
Preserving confidentiality
As generative AI models gain traction, safeguarding confidentiality emerges as a critical consideration. Inadvertent sharing of sensitive information poses inherent risks.
While generative AI tools offer invaluable assistance, caution must be exercised when sharing code containing proprietary or confidential data. In a recent event chronicled by Dark Reading, engineers at Samsung shared sensitive information with ChatGPT while trying to debug some of their code. It is very clear in the ChatGPT FAQ that this content is stored and shared with “trusted service providers,” so even if ChatGPT is not supposed to use it except to train the model, it is there somewhere and confidentiality has been breached.
By adopting a vigilant approach and refraining from sharing sensitive code, developers proactively ensure the confidentiality of their projects remains intact.
Mitigating IP concerns
Protecting intellectual property lies at the heart of responsible generative AI usage. Generating code that unintentionally infringes upon existing copyrighted code necessitates careful attention. Adhering to ethical standards and legal requirements requires thorough review and potential modification of the generated code to ensure originality and compliance.
There are a few concrete steps that developers can take to minimize the risk of IP infringement:
- First, use generated code on non-strategic code only. The risk of using copyrighted code on core IP is very high, so developers who are working on core IP should use generative AI on non-critical code only: test, demo, configuration, and examples and tutorials.
- Second, to avoid using open-source code, without proper attribution, developers can utilize Software Composition Analysis tools like Synopsys Black Duck. These tools scan code for known open-source snippets and flag if open-source code appears in a software application.
- Third, it is good practice to use code snippets from generative AI as suggestions or ideas and rephrase them for the problem, instead of bluntly copying the full code. By iterating with human-in-the-loop, developers can achieve something truly original.
By prioritizing these precautions, developers protect their intellectual property rights while demonstrating respect for the rights of others.
Embracing the power & responsibility of generative AI
Generative AI is significantly transforming engineering by boosting productivity and the overall prototyping process, but it cannot replace human developers entirely. While tools like ChatGPT and Copilot are becoming incredibly popular, promoting responsible usage and strong coding practices is essential.
As such, developers should use caution outside their expertise and evaluate outputs within the project context. Additionally, organizations are responsible for providing clear usage standards to ensure code generation is accurate and secure and respects confidentiality concerns. Additionally, IP infringement can be mitigated with proper discipline and tools.
Industry best practices are still to be defined, as this technology is relatively new to public usage, but some recommendations include customized AI systems trained on in-house code or refining user prompts to enhance alignment with coding practices.
With all of these considerations, organizations can unlock generative AI’s potential while fostering a culture of innovation and accountability. So with that in mind, let’s embrace the exciting possibilities that generative AI brings to the table and have some code-generating fun along the way.
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