Industry Survey: Faster Coding, Slower Debugging

With the rapid advancement of artificial intelligence, AI-assisted programming tools like GitHub Copilot, Cursor, and Claude Code have become increasingly integrated into the daily workflows of software developers. These tools aim to boost productivity and shorten development cycles by automating code generation, providing intelligent completions, and detecting errors.

However, the adoption of AI is not without its challenges. The actual impact of these tools on the traditional allocation of time between coding and debugging is now a subject of widespread industry focus and in-depth investigation.

This article provides a detailed analysis of the shifting trends in development and debugging time overhead in an AI-assisted programming environment, examines the key driving factors, and discusses the implications for the future of software engineering.

Time Allocation in Traditional Software Development

Before the widespread adoption of AI-assisted programming, the debugging and testing phases have historically consumed a significant portion of the total project effort.

According to classic software engineering research, the integration, testing, and debugging stages typically account for 30% to 40% of the total project hours [1]. Another estimate suggests that developers spend approximately 35% to 50% of their time on software verification and debugging [2]. This means that in traditional manual coding, the time allocation between the coding phase and the debugging phase is roughly 60-70% versus 30-40%.

Although coding seems to be the main part, developers still need to spend nearly half of their time debugging and fixing problems[1][2]. For instance, Pressman's textbook notes that about 30% to 40% of project time is spent on the integration, testing and debugging[1], while a commentary in ACM Queue estimates that verification and debugging can take up as much as 35% to 50% of the time [2]. These figures highlight the critical role and high cost of debugging in the traditional software development lifecycle.

Shifts in Time Allocation After Adopting AI Assistance

With the introduction of AI coding assistants, developers widely expected a reduction in coding time. However, the reality is more complex, with outcomes varying by scenario. Several recent comparative experiments have revealed the intricate effects of AI assistance on development time:

GitHub Copilot Randomized Controlled Trial (RCT): An RCT focused on GitHub Copilot, where participants were tasked with implementing a simple HTTP server, found that using the AI tool accelerated task completion by 55.8% [3]. This indicates that in specific, controlled task environments, AI can significantly enhance coding efficiency.
METR Organization RCT: In a more realistic setting, however, the METR (Model Evaluation and Threat Research) organization conducted an RCT with 16 experienced open-source developers. One group was allowed to use Cursor with Claude AI assistance, while the other was not. The results showed that the AI-assisted group actually took 19% longer to complete their tasks [4]. This implies that AI did not accelerate the development process for these senior developers. Before the experiment, developers had anticipated that AI would improve their efficiency by about 24%, but the final outcome was a 19% slowdown for the AI-assisted tasks compared to the unassisted ones [3][4].

Data from developer community surveys also supports the view that debugging AI-generated code is more time-consuming. In the 2025 Stack Overflow Developer Survey, 66% of developers found that AI-generated code was "almost correct, but not quite," which increases the proofreading workload.

Furthermore, 45.2% of respondents stated that debugging AI-generated code is more time-consuming than debugging human-written code [5]. This data suggests that while AI can rapidly generate code snippets, developers often need to spend more time inspecting, modifying, and debugging the output, leading to no significant reduction in the overall debugging overhead.

In summary, two trends are emerging in the time distribution of AI-assisted development: a significant increase in coding speed for certain controlled tasks [3], but an increase in debugging and review overhead in real-world engineering scenarios, which can lead to a potential decrease in overall efficiency [4][5].

Key Drivers Behind the Shift

The primary factors influencing the changes in time allocation in an AI-assisted programming environment include:

Insufficient Correctness of AI Code: A majority of developers report that AI-generated code is often "almost correct, but not quite" [5]. Researchers at METR observed that AI suggestions are generally in the right direction but contain detail-level errors, requiring developers to perform additional line-by-line inspection and modification [6], which significantly increases debugging time.
Additional Proofreading and Debugging Work: Recordings from experiments show that developers using AI frequently spend time debugging and cleaning up the AI output to meet project requirements [7]. In other words, although AI can "write" code quickly, developers need to re-read and correct it due to uncontrollable errors and out-of-context segments, meaning debugging time is not reduced.
Prompt Engineering Costs: AI-assisted tools rely on natural language prompts. Studies indicate that some developers also spend time crafting effective prompts or waiting for the AI to generate results [7]. This time overhead, which does not exist in traditional coding, has now become a new source of time consumption.
Readability and Code Quality Issues: AI-generated code can sometimes lack stylistic consistency and contextual understanding, increasing maintenance difficulty. Experienced developers have mentioned that AI often produces verbose code or code that does not conform to project conventions, requiring them to "read it over a few more times to make sense of it" [8]. Data also suggests that projects that heavily rely on AI-generated code may introduce more bugs and complexity, slightly reducing delivery speed [9].
Shift in Cognitive Load: An analysis from the Cerbos blog points out that AI coding assistants create an illusion of "superficial velocity" making developers feel they are making rapid progress , but in reality, they are spending their time reviewing and understanding the AI output[8].In other words,in an AI-assisted environment, developers are shifting from traditional keyboard typing to more thinking and verification. While this lessens the initial writing burden, it does not reduce the overall workload.

Source	Traditional Development	Change After AI Assistance	Notes
Pressman (2000)	Debugging accounts for ~30%–40% of project time	—	Proportion for integration, testing, and debugging phases [1]
ACM Queue (2017)	Verification + debugging accounts for 35%–50%	—	Percentage of developer time on verification/debugging [2]
GitHub Copilot RCT (2023)	—	Completion time reduced by 55.8% (acceleration)	Simple JS task with Copilot was 55.8% faster than without AI [3]
METR RCT (2025)	—	Completion time increased by 19% (deceleration)	Experienced developers with Cursor/Claude were 19% slower than without AI [4]
Stack Overflow 2025 Developer Survey	—	45.2% find debugging AI code more time-consuming; 66% say code is "almost but not quite right"	Developer survey results [5]

Conclusion: Time Shifted, Not Saved

In conclusion, the current AI coding agents has not significantly shortened the development cycle. Instead, they often shift the time overhead to code verification and prompt engineering. Developers generally need to invest extra time to review, test, and fix AI-generated code [6] [7]. At the same time, to obtain the desired output, they must also put effort into designing effective prompts [7]. Data from the Stack Overflow survey shows that 45.2% of developers find debugging AI code more time-consuming than traditional code [5]. Field studies from institutions like MIT and Microsoft also indicate that AI tools have a minimal acceleration effect on senior engineers, while their assistance is more pronounced for novices who lack contextual experience.

Overall, the primary benefits of current AI-assisted development lie in the automation of tedious tasks and the reduction of cognitive load (such as generating boilerplate code and documentation).

However, the debugging and verification of real code still require deep human involvement [8]. In the future, to truly reduce the debugging time, on one hand, it is necessary to improve the quality and predictability of AI code, such as by enhancing the prompt techniques and integrating learning tools to reduce the need for manual re-checks; on the other hand, since information always decays during transmission, both humans and AI inevitably leave bugs in their programming, so stronger debugging tools are needed to assist in solving these problems. Before that era arrives, programmers may still have to continue digging in the "pit" created by AI, diligently practicing the skill of "spotting errors".

References

[1] Pressman, R. S. (2000). Software engineering: A practitioner's approach (5th ed.). McGraw-Hill.
[2] ACM Queue. (2017). Developer time allocation in software development. ACM Queue, 15(3), 35-50.
[3] Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. arXiv preprint arXiv:2302.06590.
[4] Becker, J., Rush, N., et al. (2025). Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. METR (Model Evaluation and Threat Research).
[5] Stack Overflow. (2025). 2025 Developer Survey: AI Search and Debugging Tools.
[6] Tong, A. (2025). AI slows down some experienced software developers, study finds. Reuters.
[7] Rogelberg,S. (2026). Does AI increase workplace productivity? In an experiment, a task for software developers took longer.Fortune
[8] Dziuba, L. (2025). The Productivity Paradox of AI Coding Assistants. Cerbos Blog.
[9] Munteanu, N. (2025). Developer productivity statistics with AI coding tools (2025 report). Index.dev.