Source URL: https://developers.slashdot.org/story/25/07/12/016247/ai-slows-down-some-experienced-software-developers-study-finds?utm_source=rss1.0mainlinkanon&utm_medium=feed
Source: Slashdot
Title: AI Slows Down Some Experienced Software Developers, Study Finds
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Summary: A recent study by METR reveals that using AI coding assistants like Cursor can actually slow down experienced developers when working in familiar codebases. While developers initially believed AI would expedite their work, the findings showed a 19% increase in task completion time due to the need for correction and oversight of AI-generated suggestions.
Detailed Description: The study conducted by METR aimed to assess the impact of AI tools on the performance of seasoned software developers. Here are the key points from the study:
– **Unexpected Slowdown**: Contrary to developers’ expectations of a 24% decrease in task completion time, the research indicated a 19% increase in the time taken.
– **Need for Corrections**: The primary reason for this slowdown was that developers had to spend time reviewing and correcting the suggestions made by the AI, which were often directionally correct but not precise.
– **Study Design**: The research focused on experienced developers using Cursor, an AI coding assistant, on projects familiar to them.
– **Participant Sentiments**: Despite the increased completion times, most developers and the study authors continue using Cursor. The authors suggested that the AI enhances the development experience, making it more enjoyable, similar to editing rather than creating from scratch.
– **Different Impacts for Varying Experience Levels**: The authors cautioned that the observed slowdown might not hold for junior engineers or those examining unfamiliar codebases.
– **Broader Implications**: This study challenges the narrative that AI tools reliably speed up development processes, highlighting that user experience and context significantly influence the effectiveness of AI in coding tasks.
In summary, while AI tools are often hailed for their potential to augment productivity in software development, this study reveals a more nuanced picture, emphasizing the importance of understanding the context in which these tools are applied. For professionals in AI and software security, these findings underscore the need for thorough assessments of AI-assisted programming practices and the importance of developing strategies that account for potential pitfalls in AI integration.