Machine Learning and Deep Learning advancements are incredible. Although, these technologies mainly remains under the radar of the general public. But there are few of them we just can’t ignore.
Recently, OpenAI project revealed AI that has strong abilities in writing, translation, and summarization of text. There are also ready-to-use ML algorithms that can code HTML/CSS pages out of graphic designs.
And of course, there are several Machine Learning tools that can help make source code better, secure, and agile. We hope this will help us with source code repository analysis, testing automation, and will raise the overall agility of our software projects.
When it comes to source code quality there is a lot of terminologies that developers use to asses code.
Over the years of programming, the software world created numerous debugging tools: an issue tracking system, code review software, refactoring, and testing tools. But as it turns out the source code quality resides on the same level over the years. Partly because of the market experience the influx of young programmers. Partly because the existing tools can’t detect bugs over their programmed limits.
Only 10% of the sampled defects/rules from existing static analysis tools overlap with the large range of defects and suggestions AI review platform can detect.
Application of AI in code testing also leads to faster development-to-release cycles. Here’s how.
Many software development companies try to implement continuous development and continuous automated process. But, it can not as cost-effective as it seems at first glance. In most cases, it’s much cheaper to hire a few skilled QA specialists.
Based on the World Quality reports, the average level of automation for test activities is around 16%. The primary reason for that is a high price for test automation.
Comparing, integration of AI in the testing process is prospected to be much cheaper than today’s automation. This is because developers would spend less time coding automation algorithms.
AI comes as a mechanism to make test processes intelligent. Essentially, what AI is doing is anticipate bugs based on the previous data opposite to automated testing which is bound to one project data only.
The number of publicly disclosed data breaches have risen from 780 in 2015 to 1479 in 2017. And it keeps growing every year. Obviously, such problem occurred because manual testing tools (and security automation) can’t handle ever-expanding code base developers created.
On the other hand, there are a few big problems you’ll need to overcome if you want to use AI in cybersecurity.
Obviously, application of AI for cybersecurity is still a die-hard problem. That’s why we’d advise you to wait a couple of years before getting in this field. And, maybe, spend resources on simple but more effective security automation platforms.
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