A 2018 study by Adobe revealed that, at the time, 47% of digitally mature organizations already had a defined AI strategy, with 84% also having a personalized strategy within their mobile app experience. Organizations all over the world are taking steps to move toward more AI-enabled tools and transformative experience in all company areas. At this point, it is only logical to start discussing the impact artificial intelligence has on software development.
According to a Forrester Research survey, conducted on 25 application development and delivery (AD&D) teams, the adoption of AI in IT is expected to improve planning, development and especially testing. Machine learning and NLP techniques can successfully be used to accelerate the traditional software development life cycle.
Automatic generation of code
The above mentioned Forrester report also points out that artificial intelligence is able to generate new code. How is this possible? Using NLP. Developers can add the business requirements into natural language which is then converted into an executable language for machines. Based on this, the AI system can generate the code needed to implement the functionality. In some cases, it can also bring its own idea and write the code for implementing it.
A developer’s life is not only about writing clean and decoupled code that results in interactive and user-friendly solutions. It also requires a lot of time and effort put into reading documentation, debugging and fixing issues. With the help of machine learning and smart coding assistants, developers can quickly receive feedback and suggestions for improvement for the code they have written, which helps them save a lot of time. Java’s Codota and Python’s Kite are examples of such assistants, but probably the best known at the moment is Microsoft’s IntelliSense, shipped with Visual Studio and nicely integrated into it.
Another use of machine learning in helping developers and the organizations save time and costs is by analyzing code – faster and more accurately – and identifying potential areas for refactoring.
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