The article provides an in-depth examination of the capabilities and limitations of Copilot in code generation. Multiple studies reveal a blend of positive findings, like a 91.5% success rate in generating valid code, yet highlight significant concerns surrounding code quality, including the presence of code smells and high-risk vulnerabilities in suggestions. Researchers emphasize the importance of careful validation and understanding of Copilot's outputs, noting mixed results across various programming contexts which indicate both the potential utility and risks of reliance on AI-driven code solutions.
Siddiq et al. (2022) noted that code smells were prevalent in suggestions from Copilot, identifying 18 distinct types highlighting potential quality issues.
Yetistiren et al. (2022) found Copilot's success rate for generating valid code to be 91.5%, demonstrating its potential reliability for developers.
Pearce et al. (2022) identified that 40% of Copilot's suggestions contained high-risk vulnerabilities, raising concerns about security in autogenerated code.
Nguyen and Nadi (2022) concluded that Copilot's suggestions consistently yielded low cyclomatic and cognitive complexity across four programming languages, indicating a uniformity in its output.
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