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In theory, migrating apps to the cloud should be as simple as installing existing apps on virtual machines (VMs) running in an Amazon data center. It is a bit more challenging in practice, owing to the configuration settings used to set up these applications. There can be significant differences in how apps are configured on private enterprise servers compared with VMs in the cloud. 

More importantly, enterprises can get the most mileage from a simple migration by tuning configuration settings for the cloud. This helps cloud apps, even those just running on cloud hardware, take advantage of features like scalability and dynamic provisioning. But it is often a complicated and manual process. 

Asperitas, a cloud services company, and Cast Software, which makes software intelligence tools, have partnered to automate this process. Asperitas has an established Application Modernization Framework to help enterprises inventory existing apps and migrate them to the cloud. Meanwhile, Cast has been developing tools like Cast Highlight and Cast Imaging for analyzing software infrastructure at scale. 

Asperitas specialists will use Cast Highlight to determine an app’s cloud-readiness, open-source risk and agility. This will allow enterprises to prioritize the order in which they move apps to the cloud based on readiness and value to the company. 


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What is cloud-readiness?

Legacy applications were written to run on physical enterprise servers. As a result, they miss out on dynamic scaling features built into the cloud. Failing to take advantage of these features also eliminates many cost benefits and the ability to handle spikes in demand. 

In addition, legacy apps are often configured with relatively static configuration settings. They are written with specific on-premises environments in mind that rarely change. This impedes modern cloud development practices, which include creating new test environments for functional, performance and security testing, and then destroying them when no longer needed. 

Derek Ashmore, application transformation principal at Asperitas, told VentureBeat, “Both of these problems, and there are many more, can be traced back to how the application is written.”

Finding a needle in a configuration stack

Source-code analysis tools like Cast Highlight can automatically identify these kinds of issues at scale. Without tooling, this type of code analysis is done by hand, which takes time and labor. 

“Additionally, it’s not as accurate and is subject to human error,” Ashmore said. 

The tool can also guide customers from an application portfolio perspective. Asperitas uses Cast Highlight to help customers determine which applications to move to the cloud first. It can also identify applications that are likely to require more refactoring and will take more time. And sometimes, it finds applications that are so anti-cloud-native, they need to be rewritten. 

“We’re now better able to guide customers holistically at an application portfolio level as a result of the Cast partnership,” Ashmore explained. “While we could provide some guidance before the partnership, the breadth and depth of that guidance has greatly improved.”

Asperitas has already worked with Cast to help a large financial institution formulate its application modernization efforts. It also uses Cast to help application developers identify specific code changes to make apps cloud-native. 

Software intelligence is getting smarter

Cast has several competitors doing static code analysis, such as Veracode, Checkmarx and Fortinet. Many tend to focus on general code quality and complexity. Ashmore does not feel they are as focused on preparing applications for the cloud.

Companies have been analyzing software codebases to calculate complexity and plan software engineering projects for decades. But now software intelligence is starting to support new capabilities thanks to artificial intelligence (AI), machine learning and big data innovations.

“Software analytics will exponentially improve from where it is today as artificial intelligence is increasingly used,” Ashmore said. “With that improvement will come higher quality information about applications and their limitations and vulnerabilities. I also believe that analytics will improve from a security perspective and make it easier to catch vulnerabilities earlier in the development process.”

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