Software debugging is a large and persistent part of development work. Recent survey data shows that many developers already use AI tools for debugging and getting help, while study results show both measurable code-quality gains and important limits. The figures below cover general software development rather than C-only projects, so they provide context for C programming guides rather than a measurement of C projects specifically.
Contents
- How common is AI-assisted debugging?
- Debugging by developer experience
- Differences by developer role
- Who planned to use AI for debugging?
- Confidence, complexity, and trust
- The scale of debugging and maintenance work
- What code-quality studies measured
How common is AI-assisted debugging?
Stack Overflow’s 2024 Developer Survey measured AI-tool use among respondents who were currently using AI tools. Debugging and getting help was reported by 66.98% of people learning to code and 56.74% of professional developers. The categories describe survey respondents’ reported use, not the share of all software projects that use AI.
For comparison, Stack Overflow’s 2023 Developer Survey reported that 53.9% of both people learning to code and professional developers were currently using AI tools for debugging and getting help. A separate 2023 summary reported that 49% of developers using AI tools said they used them to debug code. These results come from different survey years and response bases, so they should not be combined into a single trend line.
| Survey measure | People learning to code | Professional developers |
|---|---|---|
| 2024: currently using AI tools for debugging and getting help | 66.98% | 56.74% |
| 2023: currently using AI tools for debugging and getting help | 53.9% | 53.9% |
| 2024: interested in using AI for debugging and getting help over the next year | 22.75% | 31.62% |
Sources: 2024 Developer Survey Insights for AI/ML and Developer sentiment around AI/ML (2023). The 2023 use-case summary is from Hype or not? AI’s benefits for developers explored in the 2023 Developer Survey.
The practical implication for debugging education is that AI assistance is already part of many developers’ workflows, but adoption figures alone do not establish that the tools find the correct defect or produce safe fixes. Those questions require confidence measures and controlled code-quality evidence.
Debugging by developer experience
The 2024 Stack Overflow survey shows a clear difference in reported debugging use across experience groups. Among respondents currently using AI tools, 69.70% of developers with less than one year of experience used them for debugging and getting help. The figure was 65.02% for developers with 1–4 years of experience and 57.54% for those with 5–9 years.
Reported use was lower in the longer-experience groups: 50.86% for developers with 10–14 years, 47.74% for developers with 15–19 years, and 47.27% for developers with 20 or more years. These percentages describe use among current AI-tool users in the survey, not the percentage of every developer in each experience group.
| Developer experience | Using AI for debugging and getting help |
|---|---|
| Less than 1 year | 69.70% |
| 1–4 years | 65.02% |
| 5–9 years | 57.54% |
| 10–14 years | 50.86% |
| 15–19 years | 47.74% |
| 20 or more years | 47.27% |
Source: 2024 Developer Survey Insights for AI/ML. The same source’s interest table points in the opposite direction for planned use among respondents interested in AI tools: 17.62% for developers with less than one year of experience, 20.92% for 1–4 years, 27.45% for 5–9 years, 32.14% for 10–14 years, 36.26% for 15–19 years, and 36.68% for 20 or more years.
Differences by developer role
AI-assisted debugging was not reported evenly across roles in the 2024 survey. Students had the highest listed use at 68.02%, followed by data engineers at 66.04% and front-end developers at 62.68%. Academic researchers reported 59.06%, while full-stack developers reported 58.38%.
The middle of the role distribution included mobile developers at 54.10%, embedded-application or device developers at 54.09%, and back-end developers at 51.29%. Desktop or enterprise-application developers reported 49.52%, and engineering managers reported 49.47%.
| Role | Using AI for debugging and getting help |
|---|---|
| Students | 68.02% |
| Data engineers | 66.04% |
| Front-end developers | 62.68% |
| Academic researchers | 59.06% |
| Full-stack developers | 58.38% |
| Mobile developers | 54.10% |
| Embedded or device developers | 54.09% |
| Back-end developers | 51.29% |
| Desktop or enterprise developers | 49.52% |
| Engineering managers | 49.47% |
Source: 2024 Developer Survey Insights for AI/ML. Among respondents interested in AI tools, planned debugging interest was 31.67% for embedded-application or device developers and 31.08% for desktop or enterprise-application developers. These planned-use figures are not a forecast of actual adoption.
Who planned to use AI for debugging?
The 2024 survey separated current use from interest in using AI tools for debugging and getting help over the next year. Among professional developers who currently used AI tools, 31.62% were interested in using them for debugging and getting help during that period. Among people learning to code who currently used AI tools, the corresponding figure was 22.75%.
Experience again mattered among respondents interested in AI tools. Planned debugging interest increased from 17.62% among developers with less than one year of experience to 20.92% among those with 1–4 years and 27.45% among those with 5–9 years. It reached 32.14% for developers with 10–14 years, 36.26% for 15–19 years, and 36.68% for 20 or more years.
This split matters when reading adoption statistics. Current use and future interest are different survey measures, and the 2024 values should not be interpreted as a guaranteed growth rate or as a forecast of the debugging tools market.
Confidence, complexity, and trust
Usage does not equal confidence. In Stack Overflow’s 2024 survey, 34.29% of professional developers who used AI tools judged them good at complex tasks, while 44.89% judged them bad at complex tasks and 20.82% were neutral. People learning to code were more positive: 46.31% judged the tools good at complex tasks and 32.85% judged them bad.
In the 2023 Developer Survey, 42% of developers trusted the accuracy of AI-tool output in their development workflow. A separate 2023 measure found that 75% of developers expected their debugging workflow with AI to look somewhat or very different one year later. That is an expectation about future workflow, not a measured change in performance.
Trust also varied by geography among professional developers in the 2023 survey. The share who trusted AI-output accuracy was 55.2% in India, 45.0% in Brazil, 38.4% in Australia, 37.6% in Canada, 37.5% in the United States, 36.1% in France, 35.4% in the Netherlands, 35.0% in the United Kingdom, 30.1% in Germany, and 26.8% in Poland.
Source: Developer sentiment around AI/ML (2023) and 2024 Developer Survey Insights for AI/ML. The geographic figures are 2023 survey results and should not be treated as current country estimates. They also measure trust in output accuracy generally, not accuracy on C debugging tasks specifically.
The scale of debugging and maintenance work
AI-assisted debugging sits inside a broader maintenance burden. Stripe’s 2018 Developer Coefficient research found that developers reported spending more than 17 hours per week on maintenance issues such as debugging and refactoring. The same research reported approximately 4 hours per week spent on bad code, and 59% of developers agreed that the time their company’s developers spent on bad-code problems was excessive.
Stripe also reported an average of 13.5 hours per week spent on technical debt. These are 2018 survey measurements, not current estimates, and the categories may overlap in practice. They should be used to describe the original study period rather than to claim that today’s developers spend the same amount of time.
Source: The Developer Coefficient.
What code-quality studies measured
GitHub’s randomized 2024 code-quality study provides controlled evidence, but its scope is specific. It involved 202 developers with at least five years of experience writing API endpoints. The results are not measurements of production C programs or of every debugging workflow.
Developers with Copilot access had a 53.2% greater likelihood of passing all 10 unit tests. Copilot-assisted code contained 4.63 mean code errors, compared with 5.35 without Copilot. Developers wrote an average of 18.2 lines per code error with Copilot versus 16.0 without it, a reported 13.6% difference.
The study also reported higher ratings for several code characteristics when code was written with Copilot: readability was 3.62% higher, reliability was 2.94% higher, maintainability was 2.47% higher, and conciseness was 4.16% higher. Developers were 5% more likely to approve code written with Copilot.
Source: Does GitHub Copilot improve code quality? Here’s what the data says.
For C programmers, the useful boundary is clear: these results support testing, review, and measurement rather than automatic trust. Survey evidence shows substantial use of AI for debugging, while the controlled study reports outcomes from a defined task and participant group. Neither establishes that an AI-generated diagnosis is correct for a particular C bug, compiler, memory error, or production system.