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5 ways to use AI in app development
Artificial intelligence (AI) has taken the world by storm in recent years.
App development is no exception.
If you’re a software or QA engineer, AI can help you with repetitive tasks and increase your productivity.
This helps you build more efficient and robust apps.
And that’s just one of the many benefits of AI in software development.
In this article, we’ll discuss 5 ways you can use AI app development to build better products, faster.
Table of Contents
AI-powered code analysis and debugging
Code analysis and debugging are key methods QA engineers use to test the quality and reliability of apps.
Doing this manually can be time-consuming, though.
Engineers might miss subtle issues with the code, too.
By automating and augmenting these processes, AI technologies help speed them up.
They also minimize the risk of human error.
So, how exactly can you use AI to help with code analysis and debugging?
One way you can use AI tools is when doing code reviews.
AI-powered code analysis tools use machine learning (ML) algorithms to scan the source code.
They can identify:
- Potential bugs
- Security vulnerabilities
- Performance bottlenecks
They perform static code analysis and pattern recognition to detect common coding mistakes and anti-patterns.
Flagging these problems early in the development cycle helps make your code cleaner and more maintainable.
ML models also help predict potential bugs and defects by learning from your code repositories and bug databases.
They analyze patterns from previous bugs and bug fixes and identify areas where bugs are likely to crop up in new code.
This can help you identify high-risk areas and help focus your testing efforts.
AI can also help spot opportunities for code refactoring by analyzing your codebases.
source: Mad Devs
The improvements and optimizations it suggests will help improve your code’s:
Automating refactoring based on AI recommendations will help you avoid new bugs.
It’ll also improve the quality of your code.
You can also integrate AI-powered code analysis tools into your Continuous Integration/Continuous Deployment (CI/CD) pipelines.
They’ll automatically run checks to make sure your code complies with security guidelines and best practices.
This’ll enable you to quickly identify and resolve any issues before they reach production.
Automating testing and quality assurance (QA) using AI
Would you buy a product littered with bugs? Or trust the company that put it on the market?
Of course you wouldn’t.
That’s why quality assurance (QA) is essential.
QA is one of the most important processes in app development. AI tools can help you make it more efficient.
So, how do they make that happen?
One way is automating various testing methods, such as:
- Unit testing
- Integration testing
- Regression testing
- Performance testing
You can use machine learning algorithms to identify test scenarios, create test scripts and run them efficiently.
Test automation speeds up the testing process and allows you to get feedback on code changes faster.
Another way you can use AI in QA is generative testing.
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AI can analyze your product’s code and behavior to generate test cases.
It explores different execution paths and edge cases a human tester might miss.
This leads to more thorough test coverage and will help you discover hard-to-find bugs.
AI tools learn from previous test results and user interactions to spot areas that need more attention during testing.
They do this by prioritizing test cases based on:
- Code changes
- Usage patterns
- User feedback
This way, you’ll ensure you thoroughly test the critical parts of your software product.
The point of software testing is to identify and solve bugs in your product.
A key part of that process is bug/defect triage.
source: Software Testing Help
Bugs and defects are reviewed and prioritized based on the following criteria:
By automating this process, ML models can help you more efficiently analyze and prioritize bug reports.
AI tools also help with another key process in software testing – test maintenance.
This involves repairing tests so they’re up to date with code changes.
ML models help identify obsolete tests and suggest updates.
This ensures your tests stay effective throughout your product’s lifecycle.
AI tools help speed up your software testing and make it more efficient and thorough.
This’ll help you build a reliable software product faster.
Using Natural Language Processing (NLP) for requirements gathering
You can’t develop an app without knowing what you’re building and why you’re building it.
That’s why requirements gathering is a key part of building your product.
In simple terms, it’s the process of identifying your product’s exact requirements from start to finish.
Natural Language Processing (NLP) tools can help you quickly extract and analyze information for your requirement document.
Let’s say you have a lot of feedback from stakeholder interviews that you need to analyze.
NLP tools can summarize the key points from all those interviews, saving you a lot of time.
More often than not, requirements are expressed in natural language.
This leaves room for ambiguity and misunderstanding.
You can use NLP to analyze unstructured textual data and draw out the most relevant information.
This is especially useful if you’re building a complex product and need to process large volumes of data.
Let’s say you’re a solution architect in charge of writing the requirements document.
You’ll likely have a lot of user feedback and e-mails from stakeholders to go through.
Reading all of the feedback and identifying the key points could take you several hours.
NLP tools cut that down to mere minutes.
So, how do they do that?
NLP tools use keyword extraction and sentiment analysis to summarize the data.
Having a summary of the key points will help keep everyone on the same page.
They’ll also better understand the key requirements of your product.
You can also use NLP tools to turn the data into a more structured format, like user stories and use cases.
NLP tools identify the following in the raw textual data:
They use techniques like Named Entity Recognition (NER) and Part-of-Speech (POS) tagging to do this.
Pictured below is an example of how NER works.
source: Towards Data Science
That’s what allows them to turn unstructured textual data into user stories and use cases.
Using them to do this will save you a lot of time and minimize biases during the requirements gathering process.
A less obvious use of NLP when gathering requirements is translation.
Language barriers are a common stumbling block on international projects.
Your client might not fully understand requirements written in English, especially if they’re jargon-heavy.
NLP translators like DeepL can quickly translate them from one language to another.
This way, you’ll ensure smooth communication between everyone working on your project.
Automating repetitive tasks with AI
“Lost time is never found again.”
This quote by Benjamin Franklin still rings true today.
No one wants to be stuck doing repetitive tasks day after day, especially when their talents are better used elsewhere.
AI-powered tools automate repetitive tasks and increase your productivity.
This means you can focus on solving more complex problems during app development.
So, how can you use AI to do this?
The most obvious way is using AI to generate code.
AI models can learn from code repositories and generate code snippets to perform a certain task.
This is especially true of models based on deep learning.
source: Singapore Computer Society
You can train them on your data and generate code that’s tailored to your specific needs.
Of course, AI can’t write complex code as well as a human programmer – not yet, at least.
But it can automate the creation of boilerplate code and routine functions.
This saves you a lot of time and cuts down on development time.
AI tools, like GitHub Copilot, can even turn natural language prompts into coding suggestions.
You also get intelligent autocomplete suggestions which can significantly speed up your coding.
AI algorithms predict the context and purpose of your code to offer relevant suggestions.
You can then write a line of code with a single button.
Another way you can use AI to increase your productivity is by using AI-powered collaboration tools.
Chatbots are a great example of this.
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There are plenty of open-source frameworks out there you can use to build your own.
You can train it on your data and documentation so it serves your organization’s specific needs.
For example, you can use it to onboard new team members more efficiently.
They’ll be able to use the chatbot to get instant answers on common questions they might have about your product.
They’ll also be able to access documentation and code references.
Having that information at their fingertips instantly can save you a lot of time and solve issues more quickly.
Predictive analytics and maintenance
Predictive analytics is one of the most useful ways you can integrate AI into your app development process.
Knowing where issues are most likely to happen is invaluable for preventing problems with your software product.
AI tools use historical data and ML algorithms to predict potential issues.
They use data from:
- Bug reports
- Performance metrics
- User feedback
Predicting problems before they happen allows you to address them before they impact your end-users.
This reduces the risk of critical bugs reaching production.
You can also use AI tools to identify product components that need to be updated or replaced soon.
Predictive maintenance helps you stay ahead of maintenance needs.
This’ll minimize downtime and disruptions and improve system reliability.
One major benefit of that is increased user satisfaction with your product.
After all, user satisfaction should be the main goal of your product.
You can use AI tools to directly achieve that goal by predicting your users’ behavior.
But, how does that work?
First, AI analyzes behavioral data like:
- Clickstream data
- User interactions with your product
- User preferences
Then, they use that data to predict future user actions and preferences.
This’ll allow you to tailor your features and design to better meet their needs.
AI tools can also detect anomalies in real-time system metrics and logs.
Anomalies can be a sign of major issues such as:
- Security breaches
- Performance degradation
- Hardware issues
Promptly solving issues like that before they impact your users is critical.
AI tools are also useful for resource planning and improving scalability.
By analyzing historical data, they can project future resource needs.
This’ll help you optimize your hardware and cloud infrastructure requirements.
This way, you’ll ensure that your software systems can handle increased workloads and maintain availability as demand increases.
Using predictive analytics and maintenance will help you create high-quality software more efficiently.
How to use AI in app development: conclusion
Using AI in app development is a game changer.
It can significantly increase your productivity and allow you to fully focus on your most important tasks.
To recap, here are 5 ways you can use AI in software development:
- AI-powered code analysis and debugging
- Automating testing and quality assurance (QA)
- Using Natural Language Processing (NLP) for requirements gathering
- Automating repetitive tasks and increasing productivity
- Predictive analytics and maintenance