Discuss the challenges and best practices for managing software safety in systems that rely heavily on machine learning and artificial intelligence algorithms.
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Discuss the challenges and best practices for managing software safety in systems that rely heavily on machine learning and artificial intelligence
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Solved in 3 steps
- Discuss the ethical considerations related to software quality, especially in safety-critical systems.Discuss the challenges and strategies for maintaining software safety in complex, evolving systems over their lifecycle.Explain the concept of software safety assurance cases. How are they used to demonstrate and justify the safety of a software system, especially in industries like healthcare and automotive?
- Discuss the impact of human factors on software safety and the strategies to minimize human-related errors in safety-critical software.Exists a fundamental flaw inside the software engineering process? Explain?Discuss the concept of "safety by design" and provide examples of how it can be applied effectively in the development of software-intensive systems.
- Discuss the concept of "clean code" and its importance in maintaining and evolving software systems.Explore the challenges of maintaining software safety in the context of evolving software systems. How can software safety be maintained during software updates and upgrades in safety-critical systems?Discuss the challenges of verifying and validating safety-critical software. How does formal methods, testing, and simulation contribute to the assurance of software safety in complex systems?
- Discuss the challenges and best practices associated with software maintenance and evolution.Discuss the challenges of maintaining and updating software safety-critical systems throughout their lifecycle.Discuss the importance of documentation and traceability in maintaining software quality standards, especially in regulated industries.