We all are of the opinion that AI is only about business functions and personal assistants. We hear a lot about AI-applications in customer service, visualizations, etc. But, rarely do we consider the fact that AI is, in fact, a great tool for software development life cycle.
Traditionally, we used to feed every bit of information to the computer system to make them work in a certain manner. Firstly, the team was required to sit together and analyze the requirements. Then, the engineers would hand-write every functionality in code. This is a tedious task and you can tell that just by imagining the amount of hand-coding required.
With modern tools like machine learning and artificial intelligence, the software engineer needs to prepare a domain-specific dataset for the machine and feed it to the system. This would help machines to make decisions in a certain manner and even improve the decisions continuously. Isn’t that simply impeccable?
However, it is necessary to note that critical components of development such as front-end product interfaces, data management, and security still require advanced approaches involving human intervention. The traditional SDLC processes can take advantage of machine learning and here’s how:
1.Accelerate Prototyping
We all know developing a great technology product requires months of hard work and several engineers for constant testing, bug removal, iterations, and prototyping. AI and ML can accelerate prototyping by reducing the number of technical experts required. The technology would itself predict the requirements of the product.
2. Right Recommendations
Most of the developers spend more time on debugging and documentation than development. The process even increases overheads. To eliminate that, you can use ML and AI for the right recommendations of best practices, documentation requirements, and support.
3. Effective Error Handling
Once you use ML and AI-powered systems for a while, these machines learn. The machines learn to detect errors faster than usual. This also means that error handling becomes simpler. You even get analytics to analyze how well your development is going and how well your product is going to perform.
4.Clean Code Formation
Whenever we scale, integrate, upgrade, or re-structure, the importance of clean code is realized. Why not maintain a clean code from the starting with machine learning? Technology can automatically optimize code for high usability and interoperability.
5. Correct Feature Development
A considerable amount of time is wasted on deciding the features of the product. Sometimes, many important features are added last minutes and other times, features are removed after spending days on development. To avoid this issue, AI assesses existing applications and software and then evaluate the correct features required for a software product.
6. Enhanced Software Design
Similar to deciding the features of a software product, software design requires specialized skills and experience to come up with an innovative solution. The software designers have to vision a software product, research, investigate, and develop a prototype to even assess the feasibility of a design. This requires a lot of time and efforts from the design team.
AIDA, an assistant by Bookmark, uses artificial intelligence to assess user data. Based on which, it identifies website design style, images, font, and other design aspects.
7.Advanced Software Testing
Every software application is now connected to a lot of APIs. These applications also perform countless functions. Isn’t it natural to have bugs in the functioning?
Artificial intelligence makes testing of the software easier. You won’t have to perform software test cases whenever the source code is changed or modified. AI-powered tools can automate testing by converting case scenarios in test cases. Through these, you can test performance, load, and efficiency.
8. Intelligent Programming
Over the years, researchers have tried developing a system that can create code for developers. However, this has never been successful because as soon as there is any ambiguity, the system fails. To avoid this, the designers and developers need to input a lot of details regarding the purpose of the software product. This is ultimately as much work as writing the whole code.
Artificial intelligence has removed the hassle. Nowadays, AI tools are using neural sketch learning to detect high-level JAVA patterns.
Conclusion
The major reason for using AI in software development is automated testing and easy bug detection. These two essential aspects of development are rightly optimized by AI and ML. Overall, artificial intelligence in software engineering improves efficiency, increases effectiveness, and reduces overheads.