By Lesa 
Are you a developer working on modern applications? Yes? Then, you must have come across at least one instance when you faced the need to further streamline your workflows, reduce the manual effort, or accelerate the project timelines. But how do you achieve this goal with traditional development tools? Simply said, you can’t! You have to start integrating in AI tools for developers to achieve this goal.
Unfamiliar with AI development tools? Don’t worry! We are here for you. Our article will help you understand the role of AI-powered tools in software development. During our discussion, we’ll also go through their major benefits and best practices that you must follow for the integration process with your workflow.
Why Use AI In Software Development
Before we start exploring the tools for AI software development, let us take a step back and understand that why should you even bother about implementing this process in your existing workflow. To shed more light over this segment, we have mentioned some of the major benefits of implementing this process:
- AI-powered code completion tools like GitHub Copilot and Tabnine will help you to write code faster. You can achieve this goal by accessing the intelligent suggestions and auto-completions during the test code creation process.
- You will also have access to AI-driven platforms that can analyze the code for potential issues. After the analysis, these platforms will suggest improvements and reduce the time spent on manual code reviews.
- There are AI tools like DeepCode and Codacy to identify bugs and vulnerabilities. The major benefit of this inclusion is that these tools can perform the detection process before these bugs reach the deployment stage and cause a major issue with the application infrastructure.
- Using artificial intelligence, you can enhance your test automation frameworks. This is possible with the integration of efficient test generation and test execution. AI frameworks can also analyze various human interactions and create the test cases accordingly.
- You can also rely on AI-powered project management tools to help you in scheduling, tracking progresses, and optimizing the testing workflows. These tools will also keep track of every instance that is being performed in the current testing overflow.
- Finally, artificial intelligence will also help you to optimize the cloud resources, improve CI/CD pipelines, and predict the system failures. Factors like these will be very important to ensure the productivity, maintainability, and scalability of the entire testing infrastructure.
AI-Powered Developer Productivity Tools
Based on our knowledge, we have created a list of some of the most useful AI-powered developer tools that are currently available. While creating this list, we were particularly careful to incorporate all the common use cases that the developers will currently face:
1. AI-Assisted Code Completion
To implement AI-assisted code completion, you can use tools like GitHub Copilot and Tabnine. To further understand the role of these tools, we have explained them below:
GitHub CoPilot
GitHub Copilot is developed and powered by open AI Codex. Under its hood, it is an AI-powered code completion tool which will suggest entire lines or blocks of code. It performs this process using its natural language commands and existing code context.
It also supports multiple programming languages and helps you reduce the overall time spent on writing boilerplate code.
Tabnine
Tabnine is yet another auto code completion tool that uses deep learning models to provide intelligent options to complete your test code. These options are usually tailored depending on the developer’s own coding style. You can use this option to reduce cognitive load and improve the coding speed.
2. AI for Code Quality and Review
DeepCode and Codacy are two of the most popular tools to implement code review and quality checks using artificial intelligence:
Deep Code
This tool will use artificial intelligence to analyze your test code and detect potential bugs, vulnerabilities, and code smells. You can also integrate this tool with GitHub, Bitbucket, and GitLab. This integration will offer real time feedback on the quality of the code as you continue to develop it.
Codacy
This tool performs the same purpose by automating the code reviews and identifying the vulnerabilities along with complexity issues. These two will also help you find the styling inconsistencies on the user interface of the application. Based on its findings, it will provide a detailed repute to maintain the highest quality of the test code.
3. AI in Debugging and Error Detection
Tools like Sentry and PoniCode will help you integrate artificial intelligence with test debugging and error detection
Sentry
This is an artificial intelligence-driven error-tracking tool. You can use this tool to identify and fix issues in real time. It will also assist you with very useful insights about the root cause of errors so that you can implement faster resolution during the test debugging process.
Ponicode
PoniCode, on the other hand, will use artificial intelligence to test and debug code. It generates unit tests and analyzes each case to improve the software’s robustness. You can also use this tool to ensure that every element of the user interface is perfectly operational.
4. AI-Powered Test Automation
To implement AI-powered automation testing on your application’s development and testing cycle, you can rely on tools like TestIm and AppliTools.
TestIm
This tool will use machine learning to create, execute, and maintain automated tests using as little human involvement as possible. You can also use this tool to reduce flaky test cases and improve the overall reliability of your entire testing infrastructure.
Applitools
AppliTools, on the other hand, will be the perfect option to implement artificial intelligence-driven visual testing. The result? You can easily detect user interface regressions across multiple browsers and devices. The end goal of this process is to provide the best end-user experience to your customers irrespective of their browser or device.
5. AI for Project Management and Collaboration
ClickUp AI and Jira AI are the perfect options for integrating artificial intelligence in project management and team collaboration efforts.
ClickUp AI
This tool will assist you in task management, document summarization, and content generation. The end goal is to ensure that the development teams stay organized and efficient throughout the execution process.
Jira AI
Jira AI performs the same process while including AI-powered analytics and automation. This means that you can also perform streamlined project tracking, resource allocation, and Sprint planning with this tool.
6. AI for Cloud and DevOps Optimization
Tools like LambdaTest would be the perfect options for implementing artificial intelligence in a cloud environment and also for DevOps optimization.
LambdaTest
LambdaTest is an AI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 5000+ real devices, browsers, and OS combinations. This tool will also help you for testing AI features of your application through automated testing.
Best Practices for Integrating AI Tools
Let us now divert our attention towards some of the best practices that we suggest the developers and testers to integrate within their software testing and development workflow. These practices will benefit the test creation and execution phases to ensure a streamlined workflow:
- You must analyze your development workflow and identify tasks which can benefit from AI automation. Some of the major areas that need proper attention include coding, testing, debugging, and test deployment.
- The testers should choose AI-powered tools which align with their development environment, programming languages, and team needs. You should also have certain personal preferences that will ensure that you can use the full potential of these tools.
- You should always remember that AI tools will never overpower the human expertise. Instead, they must complement the human knowledge. To implement this practice, you must review the AI-generated code, test results, and also recommendations before you actually deploy them in your testing environment.
- It is very important to periodically review the impact of AI tools on your testing productivity. You should also analyze this impact in terms of code quality and team efficiency. These insights will help you to further refine your data-driven decisions.
- Finally, you must provide training sessions for developers to effectively use the AI tools. These sessions will also help them to stay updated with all the upcoming AI technologies in the segment of software development.
Future of AI in Developer Productivity
You will be very excited to know that the segment of AI tools for developers is constantly expanding. This means that in the coming years, you will witness various new technologies and trends in this market. To shed more light over this segment, we have mentioned some of the major upcoming trends below:
- Developers will be able to enable natural language processing to write code using natural prompts and user interaction parameters.
- The inclusion of AI-powered peer programming will help improve the collaboration between human developers and AI assistants. This will help close the gap between human experience and AI productivity.
- The upcoming usage of self-healing code will allow the testers to use AI algorithms that can detect, fix, and optimize code without even the slightest involvement of human developers and testers.
- Finally, AI-driven refactoring tools will help you to improve the code maintainability and performance. These tools will be very important when you’re working with certain applications that undergo continuous improvements and updates.
The Bottom Line
Based on all the factors that we have put forward in this article, we can safely say that AI-powered tools will become unremovable in the modern software development environment. These tools will help you to massively boost the developer productivity, improve code quality, and streamline the testing workflows.
By integrating AI within your toolset, you can reduce the manual effort, improve collaboration, and focus on innovations in terms of software testing and development.
We would like to finally caution the testers that although AI tools for developers will improve efficiency, it can never completely replace the involvement of human developers. So, you must aim to maintain the perfect balance between human intelligence and artificial intelligence.
If you aim to achieve these goals in the coming years, you will be able to massively expand your target audience while constantly building on your positive brand reputation.