How AI will change the software development processes
Since its inception in 1956, AI seems to have revolutionized in every possible way like never before.
Earlier, developing a computer program required professionals to specify in advance exactly what you want the system to do and then hand over all the features of your created technology. Whereas now, both machine learning techniques and artificial intelligence are frequently used to accelerate the traditional software development lifecycle (SDLC).
The following post emphasizes on how AI will impact the present software development procedures.
If you happen to be conversing with any AI expert and you ask them what transformation we can expect in the software development field, he or she might give a general answer stating AI sustains huge potential for improving the speed and accuracy of the processes carried out in the field. That includes advanced, automated, and accelerated methods of software development. As a result, with artificial intelligence and machine learning technologies, we can now give programmers the potential to do things faster, smarter, more effectively and more intuitively.
AI and machine learning are some of the best innovative techniques that allow programmers to discover patterns, repetitions and trends in data sets in an automated way. They can then apply this knowledge to map, model and ultimately deploy functions in software applications that will be fully cognizant of the underlying data trends generated through user behavior. And increasingly, machine behavior, too.
Here are a few examples of where AI and machine learning can make a difference.
Creating an MVP fast
Traditional programming requires months of planning and preparing to jump-start a project. Apart from getting to a developed prototype, there are also the challenges of getting needed funding.
With the help of machine learning, the cycle can be shorted to a few lines of code or just a drag and drop. Take the example of creating a chatbot either by using predefined natural language libraries or using a user-friendly, no-code platform.
Managing the project
Being an experienced project manager, you can learn from past situations about delivery times, possible delays, the most common pitfalls, and other details. This is what it takes to keep up within the alloted time and budget.
As soon as all the data is stored, it can be used to train an automated system to produce accurate estimates. Deep learning is the best choice for pattern detection jobs. All you need is a detailed log of past projects, including bugs, estimates, and actual values and even user stories and reviews.
Pattern detection can go more in-depth to identify as well as classify error types. Basically, with the help of the deep algorithm flag, errors can be identified easily, which speeds up the debugging process.
As soon as sufficient training is done, the machine itself will be able to correct a wide range of mistakes automatically. Everything works much in the same way that autocorrect works on smartphones. The only potential problem here is the annoying habit of correcting things that shouldn't be changed.
Suggested auto-complete or other types of interactive documentation are some of the best tools used in modern programming environments. Your intelligent assistant will automatically speed up the developing process and provide assistance to novices when it comes to learning about the environment. It's enables a much faster process than through trial and error.
AI could act as a trainer and come up with recommendations, offer code examples, or prevent simple coding mistakes such as closing a parenthesis. A great example of such an assistant developed for Python is Kite.
Finally, testing is one of the most crucial component in the entire development cycle. With the help of AI, one can look at the past logs and generate a list of test cases which can automatically run through the system.