Value Creation
Process automation
Artificial intelligence agentsembedded in an increasing number ofapplications,artificial neural networksthat process deep learning,automationroutines integrating various systems... All of this describes part of the current reality in the field of technology. But there is another reality, also very present in companies, that connects them to the past: a vast technological legacy that will need to undergo code conversion processes.
This finding was made by the Infosys Knowledge Institute. Ina report presenting an overview of systems used by companies in the US, Europe, and Oceania, published in May 2022, it was found that 88% of them are legacy systems. The companies surveyed reported that 90% of these systems would undergoapplication modernizationprocesses by the end of 2027.
Depending on how old the software is, its architecture, and thequalityof the documentation,application modernizationprojects can take a long time, with much of that time being spent on the code conversion stage. In this text, we will provide a historical overview of the relationship between code and software architecture, as well as contextualize them with current challenges. Follow along with us!
Whether on our computers, smartphones, tablets, watches, or TVs, we use variousapplications on a daily basis. When using them, we expect the experience to be seamless, even when they need to quickly process large volumes of data with virtually no errors.
These two characteristics—ease of use and extensive processing power—are only possible in scalable and integrableapplications, two attributes that are not typically found in legacy systems. To understand why, it’s worth taking a brief look back at the history of digital product development.
The first programming languages as we know them today were developed in the 1940s, withwidely used high-level languages such as Fortran dating back to the 1950s. From that time until the late 1980s, the software architecture used to createapplicationswasmonolithic: a unified, indivisible code base that combined the user interface, business logic, and data access layers. This type of structure required the entire application to be deployed as a cohesive entity.
Even today, it is not uncommon to find monolithic systems. However, software architecture has evolved since the late 1980s: first to applications with modular design, then to service-oriented architecture, and finally to microservices-oriented architecture, commonly found in applications that require high scalability.
In order for them to be developed—or, in the case ofapplication modernization, improved—it is often necessary to convert part or all of the code to architectures and languages that adhere to the characteristics we mentioned: ease of use and ample processing capacity.
When developing new digital products or modernizing applications that will need to scale, we select languages based on the following requirements:
As we said at the beginning of this text, depending on how old the system is, its architecture, and thequalityof its documentation, one or more treatments may be given inapplication modernization projects. In some cases, refactoring the code may be sufficient. In other, more complex cases, it is necessary to rewrite the entire application. And there are certain cases that we could say are "in the middle," those that will undergo code conversion.
To define the treatments to be performed, we first analyze the code base. This allows us to understand the business logic and map dependencies, seeking to identify the most critical ones.
We then perform characterizationteststo understand the behavior of the applications in as much detail as possible. This step is essential to define which parts will undergo code conversion, which will be refactored, which will be rewritten, and which will be migrated.
Along with the characterization, we evaluated the architecture of the legacy system to determine whether and which parts would be compatible with a cloud-based version. In this analysis, one of the key priorities is the application’s future flexibility. In other words, we assessed how easy it will be to evolve the application after it undergoes the planned modifications.
Finally, we analyze which languages can be used. In addition to the characteristics we have already mentioned, we check the risks of future obsolescence of the language and avoid those that indicate they will fall into disuse in a short time.
Here at Inmetrics, we have extensive experience inapplication modernizationprojects whose primary goal is to evolve the product so that it continues to grow. Aligned with the most contemporary digital product development techniques, we combine advanced quality engineering practices withartificial intelligenceand our expertise in cloud computing to transform your legacy systems, making them lighter, faster, and ready to respond to your business needs.
If you want yourapplicationsto increase the speed, and consequently the demands, of your business,talk to us! Our team is ready to plan and develop digital solutions or "just" to convert code that will transform the way your company delivers value to the market.Click here and chat with us!
