icon-carat-right menu search cmu-wordmark
Our Research

Secure Development

Secure development refers to the set of tools, practices, and approaches that the SEI has created to identify and prevent security flaws during early development of software systems, when it is most cost effective to do so.

To create today’s software systems, developers produce billions of lines of code each year. At that volume, there’s a high opportunity for error, and it becomes harder and harder to catch those errors as the amount of code continues to increase. Even with automated testing tools, errors still manage to get into commercially available products.

Those errors come with significant costs and risks. Many research studies have shown that the cost to remove defects, including security flaws, can be hundreds of times higher after deployment. And many of those errors can also pose security risks that criminals or state agents might exploit.

Better Software Through Secure Coding Practices

The SEI’s research in secure coding focuses on ensuring that the software we use every day—such as the software that powers the systems used by the Internet of Things—remains secure and safe. The aim of our research is to reduce vulnerabilities through the elimination of coding errors by investigating how errors occur and how to prevent them. Our solutions identify and prevent security flaws during development, when the cost of prevention is much lower than during the testing phase or in post-deployment.

We are active in the programming community, and we’ve gained unique experience and knowledge from auditing millions of lines of source code and performing audits on static analysis tools. We have combined that experience with research on the standards that define programming languages and how those languages are interpreted and compiled for runtime platforms. That work has allowed us to codify best practices and coding standards that improve the security of programming languages.

In addition, we have applied our research and experience with static analysis tools to improve their effectiveness through the development of rule checkers for several tools like Clang and Rosecheckers. We have also advanced and developed other secure development tools, as well as the Source Code Analysis Laboratory (SCALe), which audits code to identify security flaws.

We contribute our knowledge to the programming community—both nationally and internationally—through publications, webinars, blogs, conferences, and more. We also offer training—through live, instructor-led courses as well as online—to help developers, auditors, and testers learn the secure development skills and best practices we identify and develop.

What We Offer

The Latest from the SEI Blog

Detection and Repair: The Cost of Remediation

Blog Page

This year, we plan on making some exciting updates to the SEI CERT C Coding Standard. This blog post is about one of our ideas for improving the standard.

READ

Measurement Challenges in Software Assurance and Supply Chain Risk Management

Blog Page
, , and

This SEI Blog post examines the current state of measurement in software assurance and supply chain management, with a particular focus on open source software, and highlights promising measurement approaches.

READ

Latest from the Digital Library

SEI Education and Training Catalog

Brochure
Software Engineering Institute

This catalog describes SEI training and certificates that help you tackle today's software, systems, and cybersecurity challenges.

Learn More

Static Analysis-Targeted Automated Repair to Secure Code and Reduce Effort

Presentation
and

In this presentation, Lori Flynn and David Svoboda discuss the automated program repair (Redemption) project, The presentation was given at the NDIA System and Mission Engineering Conference on 29 October 2024.

Learn More

Explore Our Secure Development Projects

Secure Development Topic Page Looking Ahead

Our Vision for the Future of Secure Development

Our current and future research is aimed toward improving the efficiency of identifying and removing vulnerabilities through the advancement of tool automation, using machine learning to improve the accuracy of static analysis tools, and developing tools that identify certain classes of flaws and automatically correct them.

You can also find more information about our work in secure coding by subscribing to our newsletter.