Software engineering implies writing codes that are capable of solving deterministic problems applying logic. But owing to the recent growth of AI, the way software programmers program codes have changed recently. Thanks to the revolution of AI in software development for problem-solving, tools usage and even have changed the mindset of the programmer.
The evolution of AI has changed the software development circuit for real. Artificial Intelligence
has facilitated the processing of large amounts of data and it is an application of individual technologies operating in tandem allowing the computer to perform actions generally reserved by humans.
AI Software Intelligence has been helping the software development arena in various verticals:
How AI is used in Software Development?
AI can be used in Software Development for a multitude of reasons:
1. Automated Debugging
AI-based software and tools can be used in automated debugging through pattern detection for more and more in-depth analysis for identifying and categorizing error types. The deep learning algorithm can easily flag-off errors and speeds up the debugging procedure.
It can even shadow a software developer and even learn how to fix each one of them. Post-training, the machine will be able to automatically spot and correct a huge number of mistakes, it is similar to the auto-correct feature of smartphones.
The only issue pertaining to this is the same annoying effect of correcting which user might experience especially in the case of no correction requirement.
2. Smart assistants
The AI-based software assistants are gaining popularity day-by-day since they intelligently fasten the development process. It even helps novices to learn about the environment more speedily than the trial and error method.
AI-based software assistant tools can play the role of trainer and come up with recommendations, offer code examples and prevent simple coding mistakes like closing parenthesis.
A great example of this is Kite, assistant developed for Python.
3. Automate Code Generation
Coding from scratch is time-consuming and labor-intensive. To combat the issues of money and time constraints, researchers have built programs that can write code beforehand. But these solutions are not very good with ambiguity.
In this case, AI-based software programs came into the picture. It will reduce the workload by a significant amount. Once it masters the underlying patterns, more than code completion, an AI-based software could generate a program by putting together some predefined modules, just as LEGO pieces.
In the future, AI-based software has the potential to replace the task of junior software programmer, which is the first step towards a self-programming machine.
Throughout the development cycle, testing has been one of the most crucial elements while you build a quality software product. One of the major challenges that software programmers face while testing out software products is creating a thorough list of use-cases as well as situations that could have an impact on the program’s performance.
AI-based software tools can perform this by going through the previous logs and automatically generating a list of test cases for running through the system. It can also predict the outcomes of testing without even performing the actual tests.
It only focuses on the solution, hence saving a huge chunk of time.
5. designing strategy
Usually, software developers spend a significant amount of time deciding the features and functionality to be added to a software product.
An AI-based software solution trained on past software development projects and business factors can analyze existing software and can find a solution, fulfilling the requirements for both the clients and software engineers.
It can produce a hierarchy of the best features for the success of a solution based on user reviews of similar solutions and client marking.
Let’s dive deep into the popular software Development Tools using AI
Now that you have gone through the differences that AI makes in Software Development, it’s time for some AI-developed software tools that one should consider adding in their toolbox:
TensorFlow is an open-source numerical computational tool. This ML library is primarily for production and research.
It is backed-up by community support and hence the functionality of this tool is pretty decent. It utilizes the arrangement of a multi-layered hub that enables you to rapidly set-up, train and send counterfeit neural systems with huge datasets.
The only downside is that it takes time to learn this AI Software.
The AI-based solution can be deployed on the following:
- Edge Devices
- CPUs, TPUs, and GPUs.
H20.AI is an open-source deep learning platform. It is an AI software that is business-oriented and help them to make a decision from data and enables the user to draw insights. There are two open versions of it: one is standard H20 and the other one is the paid version known as Sparkling Water.
H20.AI is used for predictive modeling, risk and fraud analysis, insurance analytics, advertising technology, healthcare, and customer intelligence.
Cortana is an AI-enabled voice-search assistant which performs varied tasks such as answering your queries, setting reminders, and others.
Its voice recognition feature proves to be a time saver and also supports different operating systems such as Windows, iOS, Xbox, and OS.
It supports different features such as:
- Supporting a wide range of tasks like switching on the light to placing an order.
- Takes voice inputs.
- Uses Bing Search Engine.
- Support multiple languages such as French, German, English, Japanese and much more.
4. IBM WATSON
IBM Watson is a question-answering software. It gives support to SUSE Linux Enterprise Server 11 OS using the Apache Hadoop framework.
Post-training your model with Watson, it deeply understands the core notions. IBM Watson is a robust system that assists in making business processes smarter. The loophole of this AI software lies in its decentralized reporting.
Some of the predominant features of IBM Watson are:
- Supports distributed computing.
- It can operate with existing tools.
- Provides an API for app development.
5. Infosys Nia
It accumulates organizational data on the legacy system and piles it up into a self-learning knowledge base. One that business analysts and software developers in your team can leverage for creating high-performing, scalable ML models. And all of this, even they don’t possess data science expertise.
Some of the predominant features of Infosys Nia are:
- Extensibility: for data preparation, visualizations and machine learning methods.
- Self-Service Provisioning: Elastic Cloud Deployments.
- GUI-based features enable your AI solution team to build precise ML models.
- Integrated Enterprise Framework: for data preparation, deployment, and modeling.
- Streaming Fast Predictions: Infosys Nia Prediction Server.
Deeplearning4j is an open-source, customizable and distributed deep learning library written for Java and Scala. It is developed as a plug and play AI software for faster prototyping.
Some of the predominant features of Deeplearning4j are:
- It can be utilized in business environments on distributed GPUs and CPUs.
- Tailored to fit into a micro-service architecture.
- It supports GPU for scaling on AWS.
- Python, Java, and ScalaAPIs.
- Scales on Hadoop.
- Imports neural net models from other frameworks — Caffe, TensorFlow, Theano — via Keras.
- It comes with a cross-team toolkit for DevOps, data scientists, data engineers.
7. Google Assistant
It is a virtual assistant by Google which can be utilized on smartphones and smart devices. It supports the following Operating Systems- iOS, Android, KaiOS.
It even supports different languages like Italian, German, French, English, Hindi, Swedish, Russian and more.
Some predominant features of Google Assistant are:
- Browse for the information on the internet.
- Supports two-way communication.
- Schedule Events.
- Set Alarms.
- Capable of displaying your Google Account information.
- It can perform hardware settings on your device.
- It can recognize songs, objects and read visual information.
In the end
With this, I have come to the end of the blog. These are some of the top AI software development tools that you can look forward to as a platform for software developers to solve real-life problems which will make the underlying architecture better and more robust.
For more insights on AI software development, you can consult custom software development companies for project consultation, strategy evaluation, resource binding and much more.