Software bugs significantly hurt software reliability and security: causing 30% of system failures and at least 39% of the reported security vulnerabilities. In addition, with the the rapid development of ML techniques, ML-enabled systems are now becoming the new targets for malicious attacks, reliability and robustness are the key required characteristics for AI applications because of the impact they can have on human life. This course discusses a broad range of state-of-the-art software quality assurance techniques for automatically detecting, debugging, and fixing software bugs and improving software reliability and security for both general software and ML-enabled systems.
Prior background in software testing and design (EECS 4313 and EECS 3311) is strongly recommended.
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