Harness the power of AI to modernize legacy code and streamline development.

AveriSource Transform is used to generate modernized code from the legacy codebase.

The cost of AveriSource Transform is based on the size of your codebase. Please schedule a demo or reach out to us at hello@averisource.com for help with pricing.

Models of the legacy code can be generated in minutes, and it typically takes a few weeks to configure the code to be deployed.

The AveriSource Platform packages, Scan, Inventory, Discover, Analyze, and Transform incrementally build on each other so you do not need to purchase multiple packages upfront. AveriSource Inventory contains everything in AveriSource Scan; AveriSource Discover contains everything in AveriSource Inventory; AveriSource Analyze contains everything in AveriSource Discover; and AveriSource Transform contains everything in AveriSource Analyze. This incremental approach aligns value with customer needs in that you only purchase what you need. However, if you purchase one package and then realize you would like additional features, you can always upgrade to a new package.

AveriSource Transform uses AI techniques such as cluster analysis, pattern matching, templates, and programming language models to generate modernized code in a new target language.

• Cluster Analysis: Cluster Analysis is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more like each other than to those in other groups (clusters). In AI and machine learning, clustering algorithms are often used to understand and categorize data, especially when labels are not known ahead of time.
• Pattern Matching: Pattern matching involves identifying and matching specific structures or sequences within data or code. It allows for the recognition of predefined patterns or regularities, enabling efficient searching, extraction, and processing of information. Pattern matching plays a vital role in various AI applications, such as text mining, image recognition, and data analysis.
• Templates: Templates refer to predefined structures or patterns that serve as placeholders for variable elements. They allow for the creation of reusable and customizable components in software development. Templates are commonly used in AI applications for tasks such as generating dynamic content, producing adaptive user interfaces, or creating flexible data-driven models.
• Programming Language Models: Programming language models are AI models trained specifically on programming languages. These models learn to understand and generate code, aiding tasks like code completion, code generation, and automated code analysis.

These techniques enable AveriSource Transform to perform efficient data analysis, processing, and intelligent generation of new code. Our AI powered approach provides structured standardized code that’s highly maintainable.

The generated code produced by the AveriSource Transform is independent and does not depend on any specific JAR files or other libraries owned by AveriSource. This ensures that there is no "vendor lock," meaning organizations are not tied to AveriSource nor are they required to rely on and pay AveriSource for use of the proprietary JAR files for the functioning of the generated code. AveriSource Transform’s code generation functionality aims to provide flexibility and portability, allowing organizations to freely use and maintain the generated code without being dependent on AveriSource or its specific libraries.