
AI-Assisted Code Review Directly in the Development Studio
A practical showcase on how to leverage AI in the code review process with a bare minimum of external dependencies.
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When a software team decides to implement automated testing, they usually start by selecting a testing tool or framework. The choice of programming language in which the tests will be written is often overlooked or not considered at all. In reality, its selection should precede or be directly part of the criteria for choosing a testing framework.
Which programming language is the best for writing automated tests? There is no definitive answer. Everything depends on the project itself and the developers. The technologies used can significantly influence the success of implementing automated testing on a project. The choice of programming language can be assessed from two perspectives – personal and project-related.
Your knowledge and experience with programming languages is, of course, very important. It's easier, and often more practical, to choose a language you are proficient in. This will certainly help to kick-start testing and reduce the need to learn new technologies. At the same time, you can also observe which languages are popular in the field and what the current trends are. Knowledge of popular and widely supported languages can also open doors to other tools and increase your value in the job market.
Another decisive factor is the technology used in the project you are testing. Choosing a language that matches the project's technology stack has several advantages:
The choice of language should be considered from both of these perspectives. It's necessary to ensure that tests and their execution are a natural part of the entire development cycle. Anyone should be able to easily run the tests and evaluate their results. Developers should be able to not only run tests but also write them and potentially change configuration settings or execution.

A practical showcase on how to leverage AI in the code review process with a bare minimum of external dependencies.

Using a concrete example from a client project, we will demonstrate the massive impact of integrating agents across all levels of the software development lifecycle, rather than just as coding assistants. I will attempt to provide a roadmap on how to build the development process of the future (or rather, of the present).

At Edhouse, we have long-term experience in bringing hardware to life. In reality, however, such hardware is not alive; we simply provide it a software interface that allows users to communicate with and control it. But breathing life into hardware, even if only artificial life, is not a problem at all these days.
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