It is not true that AI poses a potential threat to programmers. While it may be more challenging for junior programmers to find their first job, experienced senior programmers can benefit from artificial intelligence.
In fact, rejecting AI instead of embracing it might be okay right now, but it will negatively impact the development of your product in the nearest future. It will weaken your competitive advantage - you should assume that your competitors already started to use AI tools to boost their productivity and time to market.
In short, the relationship will soon look like this: no automation and optimization = it will take longer to build a product = you will pay more for software development = you will have less budget for other aspects of product development = you might fall behind your competitors.
So, if you want to avoid this, ensure the developers you hire use AI. However, note that the ones we mentioned above are only examples. To find the best tools that will match your company's needs, always have your business's strategic goals in mind.
Automation of repetitive tasks
By leveraging artificial intelligence, developers will be able to eliminate many routine and repetitive tasks. AI can take over responsibilities such as code generation, documentation formatting, and application testing. This allows developers to focus on more creative and strategic aspects of their work.
When it comes to writing, Grammarly is many people's first choice. It's a writing assistant tool that utilizes AI to improve grammar, spelling, and writing style. While not explicitly targeted at programmers, it can be helpful for writing documentation, reports, or code comments. Grammarly automatically detects errors and suggests corrections, expediting the text editing process.
Selenium is a popular tool for automating web application testing. It uses AI to simulate user interactions with the browser, such as clicking, inputting data, and navigating through pages. This enables developers to create automated test scripts, saving time and effort compared to manual testing.
DeepCode will be returning a few more times in this article. It analyzes code for errors, security vulnerabilities, and potential threats. Using machine learning algorithms, DeepCode identifies issues and suggests improvements, assisting developers in writing secure and high-quality code.
Optimization of creative processes
Using AI-powered tools, developers can speed up the software development process. AI can analyze vast amounts of data, recognize patterns, and provide optimal solutions. This allows developers to prototype, test, and deploy applications more quickly.
TensorFlow is a popular programming framework for machine learning created by Google. It provides programming tools and libraries for building machine learning models, data analysis, and artificial intelligence. Developers can use TensorFlow for rapid prototyping, training, and optimizing AI models, speeding up the software development process.
If you want to build AI models, try out PyTorch. It features intuitive programming interfaces, facilitating prototyping and experimentation with different model architectures. PyTorch also offers capabilities for accelerating model training using techniques such as automatic differentiation and computational graph optimization.
AutoML is a tool that uses AI to build and optimize machine learning models automatically. With AutoML, developers can reduce the time required for selecting optimal hyperparameters, architectures, and cost functions. The tool explores hyperparameter space and sets the best model configurations, expediting software development.
Code Generation Tools
There are various AI-powered tools that automatically generate code based on specific specifications. These tools can speed up application development, particularly for repetitive tasks such as generating user interfaces, database handling, or data management. Examples of such tools include DeepCode and Tabnine.
AI-powered chatbots can support developers in the software development process by providing answers to questions, delivering documentation, and offering tips and suggestions for problem-solving. Examples include GitHub Copilot and Tabnine, which use machine learning to generate code based on the given context and understanding of the developer's intent.
Improving code quality
Artificial intelligence can be used to analyze and assess code quality: it can detect potential errors, provide optimization suggestions, and recommend adhering to best coding practices.
CodeClimate uses static code analysis and artificial intelligence to assess code quality. The tool automatically scans code and identifies potential errors, code duplications, poor practices, and optimization recommendations. Developers receive reports with guidance on improving code quality and enhancing its readability and efficiency.
SonarQube is an automated code analysis tool that employs machine learning algorithms to identify errors, performance issues, and code quality recommendations. The tool checks compliance with best coding practices, such as clean and SOLID principles. Developers receive detailed reports and suggestions for improving the quality of their code.
As mentioned earlier, DeepCode not only detects errors but also analyzes code to find potential security vulnerabilities and threats. It uses machine learning algorithms to scan code for vulnerabilities and propose appropriate solutions. Developers can use DeepCode to enhance the security of their applications by eliminating potential weaknesses.
CodacyCodacy utilizes artificial intelligence to automatically scan and analyze code, identifying errors, code duplications, and problematic fragments and recommending ways to improve code quality. The platform integrates with various code repositories and provides developers with detailed reports and metrics on their projects' quality.
Support in problem-solving
With advanced machine learning algorithms, artificial intelligence can analyze data, search for information, propose solutions, and provide valuable insights. This can significantly reduce the time required to solve complex issues.
IntelliSense empowers developers with a powerful code autocompletion feature available in specific code editors like Visual Studio Code or PyCharm, which utilizes artificial intelligence to suggest relevant code snippets while typing. The system analyzes the context, known variables, and available methods to propose relevant code snippets, significantly speeding up the problem-solving process.
Many of the previously mentioned products can also resolve issues developers may encounter.
Automated testing and debugging
AI can be used for automated testing and debugging of code. With machine learning algorithms, artificial intelligence can analyze errors, identify the causes of problems, and suggest fixes. This lets developers quickly find and fix bugs, speeding up the software development process.
Test.ai automates software testing. By using machine learning and image analysis, the tool can identify user interface elements, generate test scenarios, and detect potential application bugs. Developers can leverage Test.ai to accelerate the testing process and increase test coverage.
Sentry is an error monitoring tool to analyze error reports. The tool identifies patterns and causes of errors, aiding developers in quickly identifying and fixing issues. Sentry also offers real-time error tracking and reporting features.
Tabnine employs artificial intelligence to analyze context and provide suggestions during debugging. The tool offers intelligent insights on variable values, program flow, and potential errors, helping developers find and resolve issues faster.
Development of internal tools
Artificial intelligence itself is a rapidly evolving field. Hiring developers familiar with AI enables the creation of innovative solutions that harness the potential of artificial intelligence. This opens doors to exploring new technologies such as machine learning, natural language processing, or image analysis and utilizing them in practical applications.
Platforms for building custom models that significantly speed up the process and, in most cases, do not require expertise in machine learning models include:
AWS AI Services
Pre-trained and ready-to-use AI services for various applications and business processes. These services facilitate easy integration with applications to solve common problems like personalized recommendations, customer service modernization, enhanced security and data protection, and improved customer engagement. Notably, AWS AI services leverage deep learning technology, also used by Amazon.com and other machine learning-related services.
Another cloud service that allows leveraging Microsoft infrastructure for AI and machine learning tasks. A significant advantage of Azure is its integration with chat GPT, which, at the time of writing this article, is the leading natural language processing model. If you are developing software that needs to support less popular languages, Azure also provides language support, including unpopular languages like Polish.
Both companies have their AI solutions in the cloud. However, as of the time of writing this article, they lag behind in terms of capabilities compared to Azure AI and AWS, in my opinion.
The rapid development of AI is more of a support for programmers than a threat. Instead of looking for where a comma was misplaced, a developer can concentrate on solving logical problems and providing better support to the business side of your venture.
Saying that juniors will have smaller chances in the coding market is an understatement; soon, they’ll become unnecessary. Thanks to AI tools, even a person without coding knowledge and mindset can now start coding at a junior level. And who knows? Maybe, you will find some rough diamonds in your team. 😉
That’s why the developers you hire should embrace and actively use the tools rather than fear them.