Artificial intelligence has made rapid strides in the past few years. We now have intelligent systems that can read and understand natural language, analyze large data sets, and even create new content in text, speech, and image form. What’s more, engineers are increasingly using artificial intelligence in software development to automate repetitive tasks, increase efficiency, improve quality, and reduce errors in their software. This technology has been so impactful that it cannot be ignored by anyone who’s looking to make a mark in software engineering. 

So, what is AI-driven automation? How can it be leveraged in software development? What problems does it solve? Let’s explore all that in this article.

What is AI-Driven Automation? 

Software development is often replete with repetitive and mechanical tasks that not only take a lot of time but also deal with large amounts of data, which can be tedious and lead to misses and errors when analyzed solely by a human. The use of AI-driven automation in software development refers to automating various aspects of the software development lifecycle using machine learning technologies. AI algorithms and data-driven decision-making processes can help developers streamline and optimize tasks and processes involved in creating, testing, deploying, and managing software. There are many ways in which we are using AI in software engineering:

1. Analyze data: Before a software is created, a lot of research goes into understanding the requirements that the program/product is supposed to fulfil. This means gathering large sets of data, going through them, and coming up with insights. AI can process data at phenomenal speed and help analysts accelerate their research.
2. To help generate code: Developers can take the help of AI to write code. AI systems and bots can not only generate code snippets but also make suggestions to complete a code and check it to find and address bugs. Their ability to process commands in natural language has made it easier to code. ChatGPT can even translate code from one language to another. 
3. Test software: Engineers need to test their software before launch. They make and modify several test cases. This process often involves multiple rounds of repetitive tasks, which can now be automated thanks to AI. AI can help engineers write test scripts and can run thousands of tests within a few moments. Not only that, but AI can also modify test scripts based on how the application changes over time. 
4. Detect and resolve bugs: To prevent failures and glitches, engineers can also use AI to detect bugs in their programs. This also reduces manual effort on their part. 
5. Optimize performance: Automation can also help ensure the smooth and efficient working of the software. AI can identify blockers and help optimize for a leaner and more economical product.
6. Security: AI can also detect security loopholes and susceptibility to threats. Based on its assessment, it can also suggest fixes to the code and continue to monitor performance from the perspective of security. 

Benefits of AI-Driven Automation

It is not as if AI has always been around to do these tasks. Software engineering long predates automation. Historically, humans have performed these tasks manually. Then why are we increasingly offloading them on machines? There are many advantages to AI-assisted software development:

Improved efficiency: The best thing about automation is how it can take care of the most tedious and repetitive tasks, leaving developers to focus on more complex tasks from a strategic perspective. 

Accelerated development cycles: When tasks become more efficient, it leads to significantly quicker development cycles, helping companies develop and launch products quickly.

Reduced errors: AI systems don’t just perform repetitive tasks quickly but also do them correctly every time. We cannot say the same about humans. Even when they are skilled and experienced at what they do, oversights can happen. AI can check codes thoroughly for errors, thus saving time and producing reliable results.

Cost Savings: Automation has made it possible to accomplish a lot more with leaner teams and higher ROIs. Saving time is equivalent to saving money when it comes to businesses.

Are there any barriers to automation?

Despite its numerous benefits, automation in software development is not without its challenges. Any new technology takes time and resources for people to adapt to it.  Switching to AI and scaling up with it is already having many consequences for companies and their employees. 

Job insecurity: Perhaps the biggest concern with the adoption of artificial intelligence is that many people will lose their jobs. When machines start performing tasks that were so far being done by humans, it is logical to assume that they will replace human employees, stripping them of their livelihoods. Some people have gone as far as to predict that this technology will make software engineers and coders redundant since it is now possible to completely automate coding. 

However, most experts agree that this is an exaggeration. Currently, generative AI assistants like ChatGPT need specific prompts to produce desired results and should not be expected to work on their own, especially for very complicated tasks.  To achieve all this, we still need developers who understand the context and the requirements from the program while also being alert to errors. 

In a paper published in 2018, Erik Brynjolfsson, Tom Mitchell, and Daniel Rock propose that instead of debating whether AI will replace certain jobs completely, it makes more sense to examine how AI adoption will lead to the redesigning of jobs. This is because it would economically be more beneficial for AI to replace certain tasks, not whole jobs. 

Security risks: Another challenge of using AI in web development and software engineering is to ensure data security. Artificial intelligence works from a combination of logic and datasets. Firstly, machine learning requires extensive data, the likes of which most companies struggle to provide. If the data set is limited, it can even lead to biases in AI. Secondly, sharing sensitive data, like customers’ details or IP with AI is an inherent risk, which attackers may try to exploit using these same tools. 

To mitigate such risks, companies should ensure their employees are prepared to deal with the complexities of these tools with proper training and education. Another good strategy is threat modeling, in which security and development teams can proactively anticipate risks to their valuable assets and address potential threats before they arise.

Best practices and the way forward

AI is a technology that the world is still trying to come to terms with. To ensure that we keep up with the developments while mitigating the obstacles, there are many best practices that organizations and developers can follow. Education and upskilling are critical to this preparation as engineers need to learn skills like prompt engineering, math, statistics, machine learning, data engineering, and data science, among others. Online bootcamps are helping working professionals learn all these skills.

Companies should also take measures to guard against the risks of AI use, like data security risks, copyright issues, errors, and ethical issues. Further, since this technology is evolving at a rapid pace, companies should have strong change management strategies in place that let them reorganize and redesign their roadmaps in line with the new developments and prepare their employees for the future.

Overall, AI driven automation is enabling engineers to get a lot more done in less time and to use their skills for more strategic concerns. From generating code to analyzing data, and optimizing product performance, AI has permeated each stage of the software development cycle and accelerated it on the whole. It is now up to developers and companies to leverage this technology and participate in this progress while also mitigating the risks associated with it. 

Author Bio:

Anjani Vigha is a technical as well as creative content writer at Thinkful, a Chegg service. She is an outgoing person, and you will find her near books, arts and explore the miraculous world of technology. Connect with her on LinkedIn or Twitter.