How AI will impact software development

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The AI industry is never going to run out of the need for tech-savvy developers who can think out of the box. This technology is here to help us create better software which is safer than software created under traditional environments. In this article, Alycia Gordan explains why AI will teach developers a new mindset about the field they have been most passionate about.

There is a 50 percent chance that machines will outperform humans in all tasks within 45 years, according to a survey of more than 350 artificial intelligence researchers. NewScientist also estimates that machines will be better than us at:

Translating languages (by 2024)
Writing essays (by 2026)
Driving trucks (by 2027)
Writing a bestseller book (by 2049)
Automating all human jobs (next 120 years)
‘AI bots’ is not a fancy buzzword anymore; it is a reality for many businesses. Robotics and artificial intelligence are going to take over the world in the coming years, and experts are striving day and night to make that happen.

Mobile apps have already changed the way we dealt with technology. Internet of Things has brought technology into our homes, and tasks like switching off lights can be handled through an app. However, the next step will be crossed by artificial intelligence (AI). These technologies are becoming faster and more affordable for users around the world.

The software has been the basis for all the advancement we see in our lives. Be it Snapchat with all its augmented reality offerings, or Amazon’s drone deliveries, the software makes things happen. Forrester Research surveyed 25 Application Development & Delivery teams, and the respondents were positive that Artificial intelligence would improve Automation Testing Software, Agile test automation, development and the way bots can work with the help of software. These bots can become experts in the software faster than any human can imagine being, speeding up daily tasks and boosting productivity.

Helping developers
The disruptive technology of Artificial Intelligence has the potential to make developers smarter. Machine learning will improve the way we deal with daily tasks. Combining it with weaker technologies like knowledge representation can strengthen AI. Even with the Agile and DevOps initiatives, turning an idea into code is a big hurdle for many developers. AI can solve this problem by having expert systems suggest possible changes in code and how to apply them to a software development life cycle (SDLC). AI can also enable stronger text recognition in any software model. Developers will be able to get the stronger code out of this sharp recognition.

Automation has turned testing into an easier process; now AI will make it easier. DevOps teams have to spend a lot of time trying to pick the reason why something is not working and how to make things work. AI will help developers find out data, the person who worked on that data and will bring up past development life cycles for reference. This smart process can bring up flaws and previous error phases, so the current project can be improved.

Stronger applications
Our mobile phones, tablets, and desktops have faced a new generation of technology where applications can talk, hear, sense and think on your behalf. Vendors who use these apps are growing because businesses would love to incorporate this technology to generate more revenue. Point solutions and platforms are going to be a big hit in the coming years. We have already experienced this technology to some extent through Siri and Alexa. Next step is going to make these technologies even smarter for customers.

Traditional programming languages like JavaScript, Ruby, and Python offer the option of templating businesses policies and best practices. Rule-based learning can enable smarter implementation of these policies which are not confined to a single problem only. Expert advisors can benefit from this aspect because coding policies is an expensive task through traditional languages.

The weaker version of AI has been in the industry for quite some time, but it requires developer interference to come to reality. AI will enable applications to learn autonomously and react to scenarios.

Weak AI was weak because it used programming. A stronger version of this AI takes into account learning and implements smarter adaptation. Deep learning and correction through this disruptive technology is something the Devs are most excited about. However, no one knows the future of deep learning apps in an unsupervised learning environment.

New outlook
Machine learning and smart adaptations will teach the developers a new mindset about the field they have been most passionate about. Developing this mindset is a challenge and a gift. Traditional development model expects us to move in a linear way because of the algorithms we know. Machine learning algorithms don’t allow you to think in traditional ways.

Developers can focus on business objectives, understand business policies and look at their SDLC from a positive mindset. The software which is created as a result is highly responsive to different situations and ranges.

What about self-creating software?
The days where you simply tell a computer to create a program and take a back seat are still far. The computers are still not matured enough to produce code and a ready software all by themselves. This is one thing which should give developers some faith in their jobs. This industry is never going to run out of the need for tech-savvy developers who can think out of the box. The Artificial Intelligence technology is here to help us create better software which is safer than software created under traditional environments. However, we are going to spot a major shift in the nature of QA and development jobs.

Many developers believe that testing is the most important phase of the entire software delivery lifecycle. In fact, you should not let anyone tell you that the starting point of automation is by manual test cases. It is essential to produce only the best quality in times of digital acceleration. Firms will implement the practices of AI to increase test automation and achieve high quality.

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