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This study conducts a Multivocal Literature Review (MLR) to explore the strengths and weaknesses of Artificial Intelligence (AI) in software engineering, focusing on four key areas: reliability, sustainability, security and convenience. AI tools improve efficiency and automation, but they also have challenges such as bias, non-determinism and model transparency. There are threats to the sustainability of AI in software engineering, such as high computational energy demands and legal constraints on proprietary and copyrighted data. Security risks can also emerge from AI-generated code, as models may produce insecure implementations that require human oversight to ensure robustness. Even with these challenges, AI seems to be gaining in popularity due to its convenience, enabling developers to streamline workflows and enhance their productivity. This study explores these strengths and weaknesses and provides insights into the future of AI in software engineering.

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Current AI-Based Software Engineering, Strengths and Weaknesses - Results from a MLR

  • 会议论文

  • 发表日期：

    2026年

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  • Jed Walshe,
  • Robert Maloney,
  • Evun Grant,
  • Carlos Conde,
  • Gerard Marks,
  • Murat Yilmaz,
  • Richard Messnarz,
  • Paul M. Clarke,
  • Andrew McCarren

摘要

This study conducts a Multivocal Literature Review (MLR) to explore the strengths and weaknesses of Artificial Intelligence (AI) in software engineering, focusing on four key areas: reliability, sustainability, security and convenience. AI tools improve efficiency and automation, but they also have challenges such as bias, non-determinism and model transparency. There are threats to the sustainability of AI in software engineering, such as high computational energy demands and legal constraints on proprietary and copyrighted data. Security risks can also emerge from AI-generated code, as models may produce insecure implementations that require human oversight to ensure robustness. Even with these challenges, AI seems to be gaining in popularity due to its convenience, enabling developers to streamline workflows and enhance their productivity. This study explores these strengths and weaknesses and provides insights into the future of AI in software engineering.