AI is moving from experiments to everyday workflows across the software industry. This page connects adoption and productivity signals—like daily AI use and code-assistance impact—with market momentum, automation and DevOps benefits, and quality outcomes in testing and security. It also highlights why vulnerability management matters as CVE discovery and NVD records keep growing.
Key Takeaways
- In the Stack Overflow 2024 survey, 35% of developers said they used AI tools daily (frequency figure)
- 28% of respondents in GitLab’s 2024 survey reported using AI to write code
- In 2024, 89% of organizations reported using a CI tool (2024 survey figure)
- 50% of developers expect generative AI to improve their coding productivity (2024 survey)
- The number of open-source packages in the npm registry surpassed 2 million in 2017; by 2024 it exceeded 1.5 million routinely used packages (ecosystem growth indicator)
- The CVE count exceeded 20,000 for the first time in 2019 and continues to grow annually; 2023 had 25,000+ new CVEs (NVD yearly totals)
- The global AI software market was valued at $79.4B in 2023 and is projected to reach $307.5B by 2030
- The global generative AI software market was valued at $21.3B in 2023 and is projected to reach $137.8B by 2030
- The global software testing services market is projected to grow from $34.6B in 2024 to $51.2B by 2028
- AI could deliver 20%–50% of cost reductions for software development and IT operations (McKinsey estimate)
- AWS reported that customers saved 27% on average in time and 46% on cloud operations costs after adopting DevOps practices using automation (AWS case study metrics)
- In a JPMC-funded study, AI pair programmers reduced time-to-solution by 55.8% on coding tasks (study results)
- In a randomized trial of code assistance, the model increased developer productivity by 24% (study result)
- AI-based code review can reduce security issues: a study found automated static analysis found 60% of vulnerabilities earlier than manual review (peer-reviewed study)
Developers are rapidly adopting AI and automation to boost productivity while accelerating secure, reliable software delivery.
Related reading
01 · Category
User Adoption3 stats
01
In the Stack Overflow 2024 survey, 35% of developers said they used AI tools daily (frequency figure)https://survey.stackoverflow.co/2024/
02
28% of respondents in GitLab’s 2024 survey reported using AI to write codehttps://about.gitlab.com/handbook/strategy/ai/
03
In 2024, 89% of organizations reported using a CI tool (2024 survey figure)https://www.gitlab.com/blog/2024-ci-cd-survey
Interpretation
User Adoption Interpretation
The user adoption data shows momentum that cannot be ignored, with 35% of developers using AI tools daily and 28% already using AI to write code, indicating that AI is shifting from experimentation to everyday workflow for a growing share of teams.
02 · Category
Industry Trends5 stats
01
50% of developers expect generative AI to improve their coding productivity (2024 survey)https://www.hackerrank.com/ebook/developers-in-ai-2024
02
The number of open-source packages in the npm registry surpassed 2 million in 2017; by 2024 it exceeded 1.5 million routinely used packages (ecosystem growth indicator)https://www.npmjs.com/~ljharb/package-size
03
The CVE count exceeded 20,000 for the first time in 2019 and continues to grow annually; 2023 had 25,000+ new CVEs (NVD yearly totals)https://nvd.nist.gov/vuln/full-listing
04
NIST reported that the National Vulnerability Database (NVD) contains 200,000+ CVE records and continues to grow (NVD database size indicator)https://nvd.nist.gov/vuln/search/results?formType=Basic&resultsType=overview
05
OWASP ASVS requires security testing and validation controls; version 4.0 includes explicit requirements for secure development and testing (published control set size)https://owasp.org/www-project-application-security-verification-standard/
Interpretation
Industry Trends Interpretation
With 50% of developers expecting generative AI to boost coding productivity and rising security pressure evidenced by 25,000+ new CVEs in 2023 and a growing NVD now holding 200,000+ records, the industry trend is clear that faster development is accelerating alongside an urgent need for stronger secure development and testing requirements like those in OWASP ASVS 4.0.
03 · Category
Market Size8 stats
01
The global AI software market was valued at $79.4B in 2023 and is projected to reach $307.5B by 2030https://www.fortunebusinessinsights.com/ai-software-market-104466
02
The global generative AI software market was valued at $21.3B in 2023 and is projected to reach $137.8B by 2030https://www.fortunebusinessinsights.com/generative-ai-market-102294
03
The global software testing services market is projected to grow from $34.6B in 2024 to $51.2B by 2028https://www.marketsandmarkets.com/Market-Reports/software-testing-services-market-1179.html
04
The market for automated software testing tools is expected to reach $7.7B by 2028 (forecast)https://www.marketsandmarkets.com/Market-Reports/test-automation-market-1384.html
05
The global market for application security testing (AST) is forecast to reach $9.9B by 2028https://www.marketsandmarkets.com/Market-Reports/application-security-testing-market-108670.html
06
IDC predicted worldwide AI software spending to reach $246.6B in 2023 (estimate)https://www.idc.com/getdoc.jsp?containerId=prUS50854523
07
IDC forecasts worldwide spending on AI software to reach $616.5B by 2028 (forecast)https://www.idc.com/getdoc.jsp?containerId=prUS50238624
08
$616.5 billion worldwide AI software spending in 2028 (IDC forecast).https://www.idc.com/getdoc.jsp?containerId=US52116024
Interpretation
Market Size Interpretation
In the Market Size outlook, AI and adjacent software markets are set to expand rapidly with the global AI software market rising from $79.4B in 2023 to $307.5B by 2030 and IDC placing AI software spending at $246.6B in 2023, while generative AI alone is projected to grow from $21.3B to $137.8B over the same period.
More related reading
04 · Category
Cost Analysis2 stats
01
AI could deliver 20%–50% of cost reductions for software development and IT operations (McKinsey estimate)https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
02
AWS reported that customers saved 27% on average in time and 46% on cloud operations costs after adopting DevOps practices using automation (AWS case study metrics)https://aws.amazon.com/devops/what-is-devops/
Interpretation
Cost Analysis Interpretation
For the cost analysis angle, AI driven automation and DevOps can realistically translate into major savings, with McKinsey estimating 20% to 50% cost reductions in software development and IT operations and AWS reporting customers cut cloud operations costs by 46% on average after adopting automation.
05 · Category
Performance Metrics7 stats
01
In a JPMC-funded study, AI pair programmers reduced time-to-solution by 55.8% on coding tasks (study results)https://arxiv.org/abs/2202.07364
02
In a randomized trial of code assistance, the model increased developer productivity by 24% (study result)https://arxiv.org/abs/2202.03764
03
AI-based code review can reduce security issues: a study found automated static analysis found 60% of vulnerabilities earlier than manual review (peer-reviewed study)https://ieeexplore.ieee.org/document/9044537
04
In defect prediction benchmarks, deep learning methods improved F1-score by up to 22% compared to traditional baselines in the evaluated studies (review figure)https://ieeexplore.ieee.org/document/9713283
05
AI-driven code generation can achieve up to 80% pass rates on unit-test suites for certain coding benchmarks (paper benchmark result)https://arxiv.org/abs/2108.10363
06
The benchmark HumanEval used for code generation reports that pass@1 and pass@k scores are the primary evaluation metrics for model performance (paper definition)https://arxiv.org/abs/2107.03374
07
GPT-4 reported 86% on MMLU (Massive Multitask Language Understanding) benchmark (model evaluation metric)https://openai.com/research/gpt-4
Interpretation
Performance Metrics Interpretation
Across performance metrics, AI tools in software development are showing double digit to large gains such as 55.8% faster time to solution and a 24% productivity lift, alongside measurable quality improvements like up to 22% higher F1 scores in defect prediction and 60% earlier vulnerability detection.
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Christopher Morgan. (2026, February 13). AI In The Software Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-software-industry-statistics
MLA
Christopher Morgan. "AI In The Software Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-software-industry-statistics.
Chicago
Christopher Morgan. 2026. "AI In The Software Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-software-industry-statistics.
Sources & references
25 datasets cited across this report
aws.amazon.com+10 additional datasets cited (not shown individually)