Challenges and Paths Towards AI for Software Engineering
AI for software engineering has made remarkable progress recently, becoming a
notable success within generative AI. Despite this, there are still many
challenges that need to be addressed before automated software engineering
reaches its full potential. It should be possible to reach high levels of
automation where humans can focus on the critical decisions of what to build
and how to balance difficult tradeoffs while most routine development effort is
automated away. Reaching this level of automation will require substantial
research and engineering efforts across academia and industry. In this paper,
we aim to discuss progress towards this in a threefold manner. First, we
provide a structured taxonomy of concrete tasks in AI for software engineering,
emphasizing the many other tasks in software engineering beyond code generation
and completion. Second, we outline several key bottlenecks that limit current
approaches. Finally, we provide an opinionated list of promising research
directions toward making progress on these bottlenecks, hoping to inspire
future research in this rapidly maturing field.
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Alex Gu
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2025-04-01T00:24:16.159Z
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Alex Gu
user
minimario
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Naman Jain
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Wen-Ding Li
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Manish Shetty
extracted_confirmed
2025-04-01T00:25:05.638Z
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Manish Shetty
user
manishs
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Yijia Shao
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Ziyang Li
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Diyi Yang
67eb322f3d9d1396663d33f6
Kevin Ellis
67eb322f3d9d1396663d33f7
Koushik Sen
67eb322f3d9d1396663d33f8
Armando Solar-Lezama
Challenges and Paths Towards AI for Software Engineering
AI for software engineering has made remarkable progress recently, becoming a
notable success within generative AI. Despite this, there are still many
challenges that need to be addressed before automated software engineering
reaches its full potential. It should be possible to reach high levels of
automation where humans can focus on the critical decisions of what to build
and how to balance difficult tradeoffs while most routine development effort is
automated away. Reaching this level of automation will require substantial
research and engineering efforts across academia and industry. In this paper,
we aim to discuss progress towards this in a threefold manner. First, we
provide a structured taxonomy of concrete tasks in AI for software engineering,
emphasizing the many other tasks in software engineering beyond code generation
and completion. Second, we outline several key bottlenecks that limit current
approaches. Finally, we provide an opinionated list of promising research
directions toward making progress on these bottlenecks, hoping to inspire
future research in this rapidly maturing field.