Accelerate your possible with source code analytics has been saved
Accelerate your possible with source code analytics
Deloitte on Cloud Blog
How can your organization quickly assess the cloud-readiness of legacy applications and make a decision on cost-to-benefit for migration?
Even as cloud adoption rates soar, it’s often difficult for companies to assess the cloud-readiness of their applications. In the previous post in this two-part series, I introduced the process of automated source-code analytics, and I described how Deloitte’s Stormfury tool can help organizations rapidly assess their cloud-readiness and develop a faster path to the cloud. In this post, I’ll be taking a deeper dive into the benefits of automated source-code analytics and looking at real-life examples of how companies are using the process to accelerate cloud adoption.
Automated source code analytics: An overview
Automated source-code analytics challenges the choice between totally refactoring, lift-and-shift, or rehosting. It provides an alternate path that leverages cloud-native design into existing application assets. With Deloitte’s Stormfury tool, companies can scan their application code for cloud anti-patterns1 across six key categories: portability, availability, performance, code quality, vulnerability, and scalability.
And, as I discussed in the last post, Stormfury performs the scan in minutes—not days or weeks (maybe months, if manual)—and recommends and quantifies steps for remediation, so that technical architects can effectively prioritize needs. The scan is objective, consistent, and virtually error-free.
The results are presented in dashboards, across stakeholder constituencies, so that issues can be understood in an enterprise context and prioritized based on strategic objectives. With this information, IT leadership can ask and answer questions about costs, risks, and benefits and determine which applications to prioritize for migration or replacement deliver value more quickly.
Putting theory into practice
The benefits of automated source-code analytics are enormous. Game-changing, even. The following case studies illustrate the real-world benefits of source-code analytics:
- Lower costs: A large public sector client wanted to perform a cloud migration of their largest Health and Human Services application to the AWS Cloud. Among the major goals was a reduction in CAPEX and OPEX. Stormfury scanned 4.4 million-plus lines of code in minutes and identified 32,000 issues—with recommendations for remediation—over the six categories we’ve discussed. The recommendations helped the agency identify areas of significant cost reduction and prioritize initiatives.
- Faster results: A global financial services company wanted to accelerate its journey to the AWS platform and adopt a cloud-first policy. Using automated source-code analytics, they were able to identify over 1,100 cloud anti-patterns in over 320,000 lines of code, prioritize issues, and remediate them based on recommendations made by Stormfury. The organization expanded the number of migration candidate applications and accelerated its migration timeline.
- Increased efficiency: A global foundation for life sciences and healthcare wanted to rapidly adopt a cloud infrastructure for their drug development operations with a next-generation R&D model that would align the data, people, and processes within their R&D organizations to increase efficiency. The assessment quickly identified over 40 anti-patterns in various quality attributes related to cloud migration across 200,000 plus lines of code. The results enabled the foundation to realign their R&D model, make the process more efficient, and secure key funding decisions.
- Lower risk: A large state health and human services agency needed to migrate a large state-based marketplace with complex functionality and a large code base to a cloud infrastructure. Using source-code analytics, they scanned over one million lines of code and rapidly identified over 700 anti-patterns that would potentially hinder migration and increase the risk of project failure. The organization was also able to integrate source-code analytics into its DevOps practice to leverage cloud-native best practices and reduce risk in their cloud-migration and application development process.
A faster path to the future
The benefits of using source-code analytics do not stop with an initial assessment for a particular project. Any time legacy applications are under consideration for the cloud, organizations can use Stormfury to quickly assess their cloud-readiness and make a decision on cost-to-benefit for migration. This speeds up cloud-migration and deployment across the enterprise—especially in an M&A scenario when potentially hundreds or thousands of applications need to be assessed and remediated.
What’s more, Stormfury can be integrated into the DevOps function so that developers are “guard-railed” into developing code that has cloud-native functionality built in. That way, future applications slated for cloud deployment can be optimized from the start.
Accelerate your possible
These benefits coalesce into a powerful singularity: automated source-code analytics helps companies accelerate what’s possible with the cloud. With its rapid-assessment capabilities and its ability to recommend and quantify remediation efforts, Stormfury helps companies leverage automated source-code analytics to get to the cloud, and deliver value with it, faster. And in a race that everyone is running to win, having speed, coupled with the knowledge of how to use it, is a winning formula.
1 Cloud anti-patterns are common code characteristics that hinder the ability of the application to maximize on cloud benefits.
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