How AI Will Change the Software Development Processes?
Artificial intelligence and machine learning in software development take various forms. With artificial intelligence and machine learning technologies, we can now give programmers the potential to do things faster, smarter, more effectively, and more intuitively. Artificial Intelligence technology is redefining how developers work, already driving big gains in productivity, quality, and speed. AI algorithms can be used to improve everything from project planning and estimation to quality testing and the user experience. Popular uses involve augmenting automatic processes with the ability to make decisions based on specific inputs or outputs.
Currently, there is a completely new paradigm for inventing technology other than the machine learning techniques which are used to accelerate the traditional software development lifecycle (SDLC). In traditional times, developers needed to specify their requirements in advanced and then hand engineer all those features in order to develop a computer program. It is possible to encode multiple tasks using traditional computer tactics before AI came into the picture.
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.
It is one of the conceptual phases of SDLC where you need human intervention at its max. AI offers a broad range of tools and techniques such as Google ML Kit and Infosys Nia for automating certain processes in order to minimize human efforts up to some point. Such an automated phase helps to detect the loopholes before moving to the design.
Design of the software
It requires specialized learning and experience when it comes to planning and designing software projects in order to propose a unique solution. Setting the correct design for each phase needs to be an error-prone task for the designers. The retracts and future investigating plan helps to make dynamic changes to the design until and unless the client reaches the desired solution. By automating a few complex procedures using artificial intelligence tools, you are allowed to enable the most capable methods in order to protect the designs.
Take an instance of AI Design Assistant (AIDA), most of the designers are able to understand the needs and desires of the client as well as use the same for designing a similar project.
Managing the project
Software development estimates are always a challenging task as it’s difficult to predict what obstacles you might encounter along the way and how much those unknown roadblocks can eat into the budget or push back deadlines.
Artificial intelligence can utilize all of its existing data from past projects and a variety of other factors that you want to include to help you determine a far more accurate software development estimate.
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.
This will make it easier for your organization to decide what projects to pursue and which projects to cut before getting started.
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 autocorrects 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 enables a much faster process than through trial and error.
Traditionally, when an error was found in software, a developer would need to go in and look to investigate what the problem is. Then, if the problem could be found, work to correct it. This process, while perfectly fine, is time-consuming and error-prone.
One of the main benefits of artificial intelligence is that it can automatically detect, diagnose, and repair software errors without a developer ever having to touch them. This helps keep costs down and greatly improves efficiency.
Another area enhanced by AI is software deployment. It is a unique stage in the development process that can sometimes be home to errors that you might miss during tests. AI-enhanced monitoring software can detect broken deployments and roll the code placed on the production branch back or forward automatically to prevent incorrect code from getting into the live environment.
Finally, testing is one of the most crucial components in the entire development cycle. With the help of AI, one can look at the past logs and generate a list of test cases that can automatically run through the system.
The role of AI in software testing is becoming more and more critical to the quality assurance process. Traditionally, quality assurance testing is a time-consuming, manual task with a high margin of error.
Rather than replacing software programmers, artificial intelligence will become a tool that software programmers use to gain new knowledge, streamline processes, and, ultimately, write better code.
Senior Developer and Architect