According to a recent study, AI-enabled solutions will generate $3.0 trillion in business value by 2021. About 85% of organizations are investing in AI-based solutions. Every parameter of the Software Development Lifecycle (SDLC) from predicting project timeline to software delivery estimation to bug fixing to coding assistance and then testing is likely to get improved by AI solutions. It has the ability to speed up the software development processes while improving the accuracy of the products.
Importance of Artificial Intelligence in Software Development
For creating business-specific software products, it requires detailed planning for placing the ideas into operation. This, however, was a long process. To make the process short and accurate, AI offered fast prototyping capabilities by evaluating the previous and current models. It came up with automated decision-making, optimized development process, early technical validation of ideas, etc. With fast prototyping AI solutions, Developers can create new applications quickly and deliver improved customer experiences.
Better Coding Assistance
As organizations keep upgrading their technologies, Clean Code is a way of creating stable software that is easy to maintain in the long-term. AI solutions help in analyzing such codes and automatically optimizing them for better performances. Moreover, the codes get more functional and can be used as a natural language style.
Automatic Bug Detection and Correction
Bug fixing plays a vital role in the software development process. AI solutions have transformed the detection and correction methods of bugs. It includes auto-correcting codebase methods by eliminating human involvement. AI can detect crucial areas of code by analyzing the previous data or coding patterns and fixes each of them with improved accuracy.
Smart AI Assistants
AI-based programming assistants are attracting immense popularity among the developers. The reason is, these assistants help developers in lowering the unnecessary workload so that they can focus more on other important parameters which can enhance the user experience. Smart AI assistants offer 24*7 support and recommendations, offering relevant documentation, best practices, code examples for developers, and assisting them in the fast development process. A popular example of an intelligent assistant developed for Python is Kite.
Software Product Maintenance and Testing Services
A huge amount of software development cost is spent on Product Maintenance. Maintaining the product requires a lot of time deciding which features to add on priority and which one to be discarded. This slows down the process of software development. To overcome this issue, AI-based solutions were executed. These smart solutions are trained on the basis of the previous models through which they decide the best performing features and implement them in the products. This helps in improving the quality and increasing the success rate of the software products.
Some Real-World Examples of “Integration of AI into the Software Development”
- The majority of the codes are modified within months, among the huge amount of codebase it becomes hectic to keep a track of bugs and errors, this is when the Bug prediction Tool is used. Google’s Bug prediction Tool “Google Bugspot Tool w3C” with the combination of Machine Learning Algorithms and in-depth statistical analysis, detects the bugs, risky codes & code lines. The tools offer classes of impactful metrics in discovering flaws (required dependencies, lines of codes, etc).
- DeepCode is an AI-powered programming tool that operates as a Coding Assitant for Software Development Projects. It helps developers in guiding how to fix the code. DeepCode improves the code by fixing and optimizing it.
- SapFix is an AI Hybrid tool deployed by Facebook. It can automatically create relevant fixes for the detected bugs. The fixes are then sent to the developers for approval and deployment to the final production.
- AI canvas is a tool for Strategic Decision making. It helps in identifying the judgments, actions, inputs & outputs, documentation, and feedback related to the development and deployment of AI solutions in software development.
- Bayou creates coding snippets for accessing APIs in Java. It analyzes the code and query from the user and then delivers the right API idiom. It helps in simplifying the software development tasks.
With the ability to increase the time to market rate, the prototyping speed & efficiency, automatically handling the error plus refactor code, precisely estimating the budget & time, and automated testing services, AI has come up with the huge potential for speeding up and improving the performance of the software development.
At Jellyfish Technologies, we create highly accurate AI-based solutions using Python programming language and deep learning with state of the art frameworks and libraries like TensorFlow, Keras, PyTorch, Theano, etc. We are experts in creating NLTK applications like chatbots, smart assistants using technologies like Dialogflow, RasaCore, RasaNLU, Spacy, and Tensorflow. We have expertise in image processing applications, classification, and regression models. Computer vision frameworks like OpenCV, Scikit-image, and TensorFlow are on our fingertips. With these technologies, it is much easier to train models with modern GPUs for classifications and multiple object detection within an image with a high level of accuracy. Our team has excelled in projects like the stock market, weather prediction, detection of spam emails, image classification, and recommendation systems using deep learning models. If you are looking out for Artificial Intelligence Development, feel free to contact us.