5 Practical AI Skills to Increase Productivity as a Software Engineer
Learn transformative techniques that improve efficiency to keep you competitive in the software career landscape.
The intersection of career and technology has become more prominent with the rise of artificial intelligence (AI), and the transformation has been nothing short of revolutionary. Various career paths, like software engineering, have integrated AI to achieve better efficiency and productivity.
As a newbie or professional in software engineering, understanding and applying specific and targeted AI tools and techniques can give you a competitive edge in developing smarter, faster, and more optimized systems.
To blend in with the transformation, this article will teach you five practical AI skills software engineers should master to increase productivity.
1. Automating Code Reviews with AI Tools
Automating code reviews is one of the most impactful AI skills you can use to enhance productivity as a software engineer, as this helps to analyze codebases for errors, security vulnerabilities, and compliance issues, ensuring that bugs are caught before they reach production.
Usually, manual code reviews are time-consuming and prone to human oversight, creating errors. For this reason, AI-powered tools like Codacy and DeepCode are endorsed to perform real-time static analysis, flagging issues in seconds and helping maintain coding standards across the team. This means fewer bugs, more efficient workflows, and faster deployment times.
2. Leveraging AI for Test Automation
Apart from code review automating skills, test automation is another AI-driven skill to learn. While it is similar to code automation, it leverages machine learning algorithms to predict potential areas of code that are error-prone and need testing. This significantly reduces the time it takes to develop, maintain, and execute tests.
In most scenarios, testing is crucial to software quality, but it can be repetitive and labor-intensive. This is why automating tests with AI tools can reduce testing cycles, allowing engineers to focus on writing and improving code rather than repetitive testing tasks.
Tools like Testim or Mabl can enable you to adapt to code changes and automatically update test cases, saving you from manually adjusting test scripts.
