Software engineering entails creating algorithms that can solve deterministic issues using logic. However, because of the recent rise of AI, the way software engineers create codes has altered. AI tools have transformed the programmer’s perspective because of the advancement of artificial intelligence and machine learning in software development for problem-solving.
AI Development & Coding Tools
Artificial intelligence has aided in processing vast quantities of data. It is an application of separate technologies working together to enable the computer to execute operations normally reserved for people to expedite the development method for a programmer. And if you’re looking for AI Development Tools for streamlining your development procedure, you’re at the right place. In this article, we’ve reviewed the finest AI development tools in the market. Let’s get going!
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What are AI Development & Coding Tools?
How Is AI Used in Software Development?
What Can AI Do in Software Programming?
What are AI Development & Coding Tools?
AI Development Tools are software applications and frameworks specifically designed to support the development of artificial intelligence (AI) systems. These tools provide developers with various functionalities, such as data processing, model training, testing, and deployment, which are critical for building robust and effective AI and Machine Learning applications.
AI development tools are essential for reducing the time and effort required for building AI applications. They offer a standard platform for developers to work on and ensure that the models are scalable, modular, and maintainable. Moreover, with these tools, you can get a wide range of resources such as documentation, tutorials, and forums, which make it easier for developers to learn and collaborate with the community.
How Is AI Used in Software Development?
Automated debugging is facilitated by artificial intelligence tools and software, namely pattern recognition for comprehensive error analysis, identification, and classification. The debugging process is sped up, and errors are more readily detected using the deep learning system. In addition, it significantly lessens the burden of code generation. If an AI-based software learns the fundamental patterns, it can construct a whole program simply by assembling a set of pre-set modules, much like LEGO blocks are assembled.
Artificial intelligence software assistants are becoming more popular due to their ability to accelerate the software development process intelligently. Even for newcomers, it speeds up the learning process. AI assistant tools may act as a tutor by suggesting solutions, providing code samples, and even preventing rookie errors like forgotten closing brackets. Furthermore, AI tools can execute the testing by reviewing the historical logs and generating a set of test cases to be executed on the system. Without doing the tests, it can also accurately anticipate the results.
Further Reading: Web Developer Vs Front End Developer >
What Can AI Do in Software Programming?
Code Generation: AI can generate code based on the requirements and specifications provided by the programmer.
Code Optimization: Automatically identify and eliminate inefficiencies in code. This can include identifying redundant or unnecessary code, optimizing algorithms and data structures, and improving memory usage. AI can also be used to predict and prevent potential performance issues before they occur. Additionally, it can automatically generate optimized code based on specific performance goals or constraints.
Bug Detection: One of the most common ways of AI bug detection is through the use of machine learning algorithms that can analyze code and identify patterns that are indicative of bugs or errors. These algorithms can be trained on large datasets of code to learn what types of errors are most common and how to identify them. Another way that AI can be used in bug detection is through the use of natural language processing (NLP) algorithms. These algorithms can analyze bug reports and other documentation to identify common patterns and keywords that are associated with specific types of bugs.
Code Completion: AI can suggest code completion options based on the context and the developer’s coding style.
Testing: Automating the testing process by generating test cases and identifying potential issues. Increasingly intelligent software testing capability to drive test execution, qualify and reproduce issues reliably, shortening the development cycle and ensuring higher quality results.
Code Review: Automating code reviews and performance optimization – using parameters that are machine-learned and avoiding human-driven repetitive regression and performance tests.
Top 11 AI Development & Coding Tools Widely Used in 2023
Want the best AI development tools for your SDLC? These tools can help:
MxNet
Mxnet is a powerful and adaptable deep-learning library. Researchers and engineers from several institutions and businesses can work together on MXNet. MXNet’s extensive Python application programming interface (API) suits Python’s large user base. When used by developers, the sophisticated tools provided by MXNet unlock the full potential of GPUs and cloud computing. While these methods and tools can be applied to various mathematical computations, MXNet focuses particularly on facilitating the creation and deployment of massively scaled deep neural networks.
AI Development Tool – MxNet
MxNet’s Key Features
- Provides flexibility and support through a hybrid front end that can easily switch between an imperative and symbolic mode.
- Research and production can use scalable, distributed learning and performance optimization.
- Scala, Julia, Clojure, Java, C++, R, and Perl support, and tight interaction with Python.
- Has applications in computer vision, natural language processing, time series, and more.
Supported Platforms: Windows, Mac, and Linux
Pricing: Free to use
TensorFlow
TensorFlow is a set of machine learning frameworks, tools, and resources. A large community of academics globally relies on this framework to create cutting-edge ML models. It is a free and publicly available machine learning library for creating reliable and effective ML-based software. Because of this tool, developers can use more than just the features offered by individual vendors to address machine learning challenges.
AI Development Tool – TensorFlow
TensorFlow’s Key Features
- Distributed model training across several devices is made possible by DTensor in TensorFlow.
- Provides compatibility with both Parallel spatial training and parallel training utilizing data partitioned across dispersed devices.
- Allows rapid debugging and model iteration with support for high-level APIs like Keras, simplifying model creation.
- Offers a set of resources aimed at streamlining developer work on AI projects.
Supported Platforms: Windows, Mac, and Linux.
Pricing: Free to use
H20
H2O is a cloud-based AI platform that allows any data analyst, developer, or data scientist to create high-quality AI models and apps in minutes, swiftly delivering AI to business customers. Before, this process may take months or even years. But its powerful automated ML features allow cutting-edge AI models and programs to be easily developed.
AI Development Tool – H2o
H2O’s Key Features
- Consists of a user-friendly AI app store that makes it simple for developers to deploy new apps to clients
- The models you build with this tool are portable between on-premises, cloud, and other infrastructures.
- Converts text to useful features utilizing techniques for extracting document characteristics like TFIDF, CNN, and GRU.
- Includes Superior ML models for spotting deceptive practices in massive data sets of transactions for application in real-time security monitoring.
Supported Platforms: Windows, Mac, and Linux.
Pricing: Free to $30 per month
Mintlify
By automation, and more especially automated documentation generation, Mintlify hopes to solve the problems associated with documentation. Using tools like NLP and web scraping, the company’s platform can read code and generate documentation that describes it. Just highlighting the code will trigger this tool to generate the documentation process. It saves time and improves your codebase by creating documentation. Moreover, Mintlify saves senior developers time in the future when explaining their code to others.
AI Development Tool – Mintlify
Mintlify’s Key Features
- Improves the document’s readability by scanning for “outdated” sections and detecting user interaction.
- It doesn’t keep any code and encrypts all user data while stored and in transit.
- Includes support for integration with well-known tools like Slack, Dropbox, and GitHub to streamline and automate development and project management processes.
- Includes a user base of 6000+ active accounts.
Supported Platforms: Windows, Mac, and Linux
Pricing: Free to $120 per month
Scikit Learn
Scikit Learn (also known as sklearn), a popular open-source machine-learning library, is designed to provide a range of supervised and unsupervised learning algorithms for classification, regression, clustering, and dimensionality reduction tasks. Scikit-learn provides a user-friendly and efficient API for building machine learning models. It includes a wide range of pre-processing techniques for handling missing data, scaling features, and transforming data into a suitable format for modeling.
AI Development Tool – Scikit Learn
Scikit Learn’s Key Features
- Includes a range of model evaluation techniques for assessing the performance of the models, such as cross-validation and grid search.
- Can be easily integrated with other tools and libraries in the Python ecosystem.
- Contains many computations for common artificial intelligence and data mining tasks, such as bunching, relapsing, and order.
- Built on top of other popular scientific computing libraries for Python such as NumPy, SciPy, and matplotlib.
Supported Platforms: Windows, Mac, and Linux
Pricing: Free to use
Rainbird
Rainbird is a cutting-edge artificial intelligence platform with a short history. The Rainbird suite includes the Rainbird Infer module, the Rainbird Flow module, and the Rainbird AutoML module, all of which may be accessed via the Rainbird Studio interface. You can use the tool’s brand-new artificial intelligence component Rainbird AutoML with machine learning for model training and deployment.
AI Development Tool – Rainbird
Rainbird’s Key Features
- Uses a robust inference engine to automate the decision-making process.
- You can get better quality forecasts using the results of the trained model with Rainbird Infer.
- The users can generate visualizations of the decision logic with the help of the knowledge maps.
- Allows for the development of comprehensive approaches for complex decision-making.
Supported Platforms: Windows, Mac, and Linux
Pricing: Free to use
Tabnine
Tabnine is an AI-powered code completion tool that provides suggestions for code as developers type. It uses machine learning algorithms to predict the next lines of code that a developer might want to write based on the context of their code and the programming language they are using. Moreover, Tabnine is designed to improve productivity by reducing the time and effort required for writing code.
AI Development Tool – Tabnine
Tabnine’s Key Features
- Supports many programming languages, including Python, JavaScript, Java, Ruby, and others.
- Integrates with popular code editors such as Visual Studio Code, IntelliJ IDEA, and Sublime Text.
- Uses a cloud-based architecture to provide suggestions for code completion, meaning that it can continuously learn and improve from the code.
- Allows developers to customize the suggestions and create their own code snippets, which can be reused across projects.
Supported Platforms: Windows, Mac, and Linux
Pricing: Free to $12 per month
Otter.ai
Otter.ai is a speech-to-text transcription tool that uses AI and machine learning to transcribe spoken words into text. It is primarily used for transcribing conversations and meetings but can also be useful for developers in software development. It can improve their communication, collaboration, and documentation processes in SDLC. Using Otter.ai to transcribe their conversations and discussions, developers can save time and increase productivity.
AI Development Tool – Otter.ai
Otter.ai’s Key Features
- Developers can use this tool to record themselves verbally discussing and reviewing their code.
- Helps in identifying errors, brainstorming solutions, and keeping track of your thoughts during the debugging process.
- Allows teammates to collaborate in the live transcript by making comments, marking significant areas, and assigning action items.
Supported Platforms: Windows, Mac, and Linux
Pricing: Free to $8.33 per month
PyTorch
PyTorch is an open-source machine learning library that is widely used for developing deep learning models. It was developed by Facebook’s AI research team and was released in 2016. PyTorch is known for its ease of use, flexibility, and dynamic computational graph, which makes it a popular choice among researchers and developers.
AI Development Tool – PyTorch
- Dynamic Computation Graphs: PyTorch uses dynamic computation graphs, which means that the graph is generated on the fly as the program runs. This allows for more flexibility and easier debugging.
- Tensor Computation: PyTorch provides a powerful tensor computation library that allows for efficient numerical computations on multi-dimensional arrays.
- Neural Network Building: PyTorch provides a simple and intuitive interface for building neural networks, with a wide range of pre-built layers and activation functions.
- GPU Acceleration: PyTorch supports GPU acceleration, which allows for faster training and inference on large datasets.
Distributed Training: PyTorch provides support for distributed training across multiple GPUs and machines, allowing for faster training on large datasets. - Automatic Differentiation: PyTorch provides automatic differentiation, which allows for efficient computation of gradients for backpropagation.
- Model Saving and Loading: PyTorch provides easy-to-use functions for saving and loading trained models, allowing for easy deployment and sharing of models.
- Visualization Tools: PyTorch provides a range of visualization tools for monitoring training progress and analyzing model performance.
Integration with other Libraries: PyTorch integrates well with other popular libraries such as NumPy, SciPy, and Pandas, allowing for easy data manipulation and analysis.
Pricing: Free to use
GitHub Copilot
GitHub Cop is a tool developed by GitHub to help organizations enforce security policies and best practices for their repositories. It uses a set of pre-defined rules to scan repositories for potential security vulnerabilities, such as exposed secrets or outdated dependencies. When a violation is detected, GitHub Cop can automatically create an issue or pull request to alert the repository owner and suggest a fix. This helps organizations maintain a secure and compliant codebase, while also reducing the risk of security breaches.
AI Development Tool – GitHub Copilot
GitHub Copilot’s Key Features:
- Code suggestions: It suggests code snippets based on the context of your code.
- Language support: It supports multiple programming languages, including Python, JavaScript, TypeScript, Ruby, and Go.
- Integration with VS Code: GitHub Copilot integrates seamlessly with Visual Studio Code, making it easy to use.
- Personalization: It learns from your code and adapts to your coding style over time.
- Collaboration: It can suggest code snippets for multiple developers working on the same project.
- Code quality: Help improve code quality by suggesting best practices and identifying potential errors.
Pricing:
- Free trial
- $10/mo for individual users.
- $19/mo for business users.
Wing Pro
Wing Pro is a professional-grade IDE for Python that offers a wide range of features and tools to help you write, debug, and test your code more efficiently. Whether you’re a seasoned developer or just starting out, Wing Pro has everything you need to take your Python projects to the next level.
AI Development Tool – Wing Pro
Wing Pro’s Key Features:
- Code Intelligence: Provide auto-completion, call tips, and go-to-definition for Python code.
- Debugging: Support remote debugging, multi-threaded debugging, and debugging of Django and other web frameworks.
- Testing: Support unit testing, integration testing, and code coverage analysis.
- Editing: Powerful editor with features like syntax highlighting, code folding, and multi-file search and replace.
- Version Control: Integrating with Git, Mercurial, Subversion, and other version control systems.
- Project Management: Wing Pro has a project manager that allows you to organize your code and files.
Pricing:
- Start from $69/year for non-commercial use
- Start from $179/year for commercial use
Frequently Asked Questions about AI Development Tools
By the end of the post, we collect several frequently-asked questions about AI development tools and software.
Do software developers use AI?
Yes, software developers use AI to enhance productivity and boost development. Moreover, AI is used in machine learning, testing, debugging, and code generation.
Does AI Development Software Require Coding Skills?
Yes, AI development software requires coding skills. Since you need to train machine learning models in artificial intelligence development software, you require a deep and thorough understanding of some programming languages and how to code them.
Will AI coding tools replace software developers?
My answer is NO, at least for now. It’s undeniable that AI tools can help developers work more efficiently by simplifying the development process. A great example of a current AI tool aimed at developers can autocomplete lines of code, add whole lines of code, or add entire functions. This will continue to be refined to improve developer productivity and manage away common errors.
However, there are problems with code generators, too. That’s why AI coding tools are used to help developers rather than to replace them entirely. For example, there is research found that developers who use AI tools are more likely to cause security vulnerabilities in their apps. Plus, questions around copyright are not entirely resolved. In other words, AI code tools are not yet completely safe to use. That said, the popularity of these tools means that they can’t be overlooked.
In the trend of AI, developers will need to have a broader base of capability as software engineering will become more of a generalist discipline with fewer routine tasks, fewer segmented specialisms, a more significant requirement for composable architecture skills, and greater general focus on performance, reliability, sustainability and security over traditional programming. Developers using AI tend to be more efficient and productive than those who don’t use it.
Overall, AI will not take developers’ jobs entirely, but it will help developers to work more efficiently and productively, saving them from writing a hundred lines of code.
Final Thought
Artificial intelligence (AI) has revolutionized the way we develop software. With the rise of AI-powered software development tools, developers can streamline the development process and create high-quality applications faster than ever. A plethora of AI Development Tools are available, from data processing to natural language processing, and we hope you’ve found the best one after reading this article. Moreover, we encourage you to share your thoughts on AI in the comments below.
