Anywhere and everywhere, it is an AI mantra. Nowadays we can’t see a business without AI, industries without AI, domains without AI, and totally the world without AI. AI’s influence is enormous.
Since its inception in 1956, AI has grown rapidly and made its historic footprints in different verticals such as healthcare, finance, food, supply chain, finance, entertainment, etc. In recent years, AI is adding another feather to its hat by creating a historic evolution in software development by helping iconic developers with repetitive tasks, data collection, data evaluation, and making decisions. Whether it is web app development or mobile app development, AI automates the different processes of development and makes it easy for developers. So, in this blog, we are going to see the different applications of AI in software development and its use cases. Let’s start.
Applications of AI in software development
Automated Code Generation
The ability to generate code automatically is one of AI’s most significant effects on software development. The time it takes to manually write the code for complicated software systems is drastically reduced with the aid of AI. This is accomplished by utilizing technologies like AutoML and AutoCode, which analyze data and produce code using machine learning methods.
The benefit of automated code creation is that it may drastically cut down on both the cost and time of development. Developers may concentrate on higher-level activities like building software architectures and creating user interfaces by doing away with the requirement to physically write code.
Automating DevOps
AI tools make software development easy, less repetitive, and more efficient. An instance of this is-, the application of machine learning Aroma, which suggests code when developers are working to find alike code snippets in a repository to find common options and errors. Facebook’s Getafix recommends solutions for fixing bugs and errors, making it easier for developers to cross the deployment hurdles and speeding up the testing process.
Proficient development roadmaps
Planning is a significant part of development because it leads the way for efficient software development. AI tool streamlines the creation of such significant roadmaps by developing project goals, missions, tech stacks, and challenges and listing highly adaptable action items.
Making tactical decisions
Because AI and similar systems are constrained by so many restrictions, intelligent systems are unable to develop software on their own. For instance, the quality of machine learning algorithms depends on the Big Data sets we use to train them. Although it might take analysts hours to assemble a usable data set using high-performing software, AI systems can answer important questions and make predictions almost instantly. AI-enabled decision-making tools can construct frameworks, develop KPIs, and assist software developers in determining which features are essential and which won’t be useful given the correct data.
Automated and easier testing process
Nowadays, it has become common for developers to use machine learning for testing. AI-powered testing bots become prevalent as they mimic a human, do a quick check of the possible use cases of the product, and find the issues along the way
These bots are fast like lightning and more efficient than human testers as they don’t get tired and work 24/7. The instance for this testing bot is the AI-powered testing tool Testsigma which can perform continuous testing in DevOps and Agile development and Eggplant which generates the model of the real-time user journey for which it automatically generates test cases.
AI as compilers
AI-powered compilers quickly will turn the code into machine language more quickly than humans. Nowadays, Software developers use the AI-based compiler called Compiler.ai to protect the code against errors by creating correctness proofs for executable code. There is another AI-powered compiler called Glow which is currently in active development by Meta.
Forecasting user behavior
Unexpected updates that need more resources than post-production bug patches might result from unexpected user behavior. By predicting how customers would engage with software based on how they have previously used iterations of the software or related items, AI can eliminate the need for these upgrades. Also, it helps developers to generate several use cases
Simply, AI is taking over tiresome tasks
Even though AI has many applications in web development, it is nowhere good enough to fully automate software development from start to end. Implementing machine learning and deep learning together to automate the whole development process without human intervention is something dangerous and potentially costs a fortune. Leveraging AI only for menial tasks like testing, debugging, and compiling would be the safe thing and reduces the time of development enabling the developers to be creative on other development activities.
Final thoughts
Leveraging AI’s Power in software development is only to reduce the time and resources spent on menial tasks. It is nowhere better enough to replace the developers nor going to take over their profession. Hence, in conclusion, for AI to work efficiently and productively in software development, human intervention is significantly needed and for the human to focus more on the creative part, AI’s role is indispensable. Two factors are tied by destinies and only if they combine, magic will happen.