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Significance of Artificial Intelligence in Software Development

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 be improved by AI solutions. It can speed up the software development processes while improving the accuracy of the products.

Importance of Artificial Intelligence in Software Development

Fast Prototyping

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 performance. Moreover, the codes become 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 fix each of them with improved accuracy.

Smart AI Assistants

AI-based programming assistants are attracting immense popularity among developers. The reason is, that these assistants help developers in lowering the unnecessary workload so that they can focus more on other important parameters that can enhance the user experience. Smart AI assistants offer 24*7 support and recommendations, offering relevant documentation, best practices, and 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 in deciding which features to add on priority and which ones 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 based on 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 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 code, etc).
  • DeepCode is an AI-powered programming tool that operates as a Coding assistant 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.

Conclusion

With the ability to increase the time to market rate, the prototyping speed & efficiency, automatically handle the errors plus refactor code, precisely estimate 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, and 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 at 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.

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