The software development world is constantly evolving. It has grown immensely with various methodologies, frameworks, and tools. In recent years, it has been widely recognized that AI can play a significant role in software development.
Yes, AI has a lot to offer to the software industry. The robotic technology adoption rate is slated to rise rapidly soon. Even 86% of businesses today are finding AI their mainstream technology.
So, any idea how AI can be helpful in software development? Can AI make a difference in the way we develop applications today? What will its future role be? This article explores all such aspects.
Here, we’ll look at the impact of AI on software development in recent times and what it will offer in the coming days. To know more, read on.
1. Automatic Code Generation: Rapid Application Development(RAD) Tool
The most significant change that AI brings to the table is automatic code generation. It takes away mundane activities like writing basic codes, fixing bugs, or generating repetitive tasks. It allows developers to reduce the overall development time and effort to a large extent.
AI takes it further and generates more sophisticated codes that are not usually written by hand. It can develop codes for complex projects in less than 10 minutes. This implies that it can create codes for frameworks like PLC or web services quickly and in a matter of seconds.
In the future, AI will generate entire programs from scratch without developers’ prompts.
Some early coding applications leveraging this technology include:
GalaBot – It generates code for Ethereum contracts.
SMACSS – Automatic code generation for Javascript frameworks like React, Webpack, or Gulp.
DeepCoder – Developed by Microsoft, it automatically uses AI to write basic programs.
2. Bugs & Error Detection: Static Code Analysis and Dynamic Testing Tools
AI has helped us discover glitches and errors in a program before release. It uses advanced algorithms to detect problems due to human negligence, typing mistakes, etc. It is beneficial for large-scale applications as these projects typically involve developers working on different sections simultaneously.
At present, AI can even suggest solutions for the errors it finds. However, it is not adept in this area yet and needs to be handled by developers.
Some early bug detection tools using AI include:
Papertrail: It detects problems through static code analysis of projects before deploying.
Sqreen: It uses machine learning algorithms for finding bugs or potential security loopholes in code.
Errorception – Distributed bug detection tool that uses NodeJS to catch programming errors.
3. Input Mechanism for Applications: Natural Language Processing(NLP) Framework
AI can be integrated with applications and automate tasks like form filling, data extraction, and other input scenarios using NLP. It allows developers to create chatbots for applications.
A bot can take user inputs through natural language input mechanisms like text, voice, or video messaging. It eliminates the need for multiple screens to transfer data or type in commands. It also makes it easier for chatbot development companies in India to create powerful chatbots quickly.
Some early NLP tools include:
Wit.ai: It offers an API for developers to create chatbots through text messaging.
Clearbit: It extracts information from emails or forms using NLP engines.
4. Improved Testing Process: AI Assistants
AI has integrated testing with human intelligence too. A lot of time is spent verifying the accuracy of test results manually, which can be automated further with help from AI.
We can program AI to check for many test cases in a concise time frame. It means there won’t be any need to do repetitive testing tasks or long hours spent away from actual work. It also helps developers find critical bugs quickly before the product is released to the market.
Some early AI assistants include:
Applitools: It uses machine learning to test UI and ensure pixel-perfect results.
Watchbot: It automates mobile app testing and checks for bugs in real-time.
5. Intelligent Code Completion: Recommendation Tools
This technology provides intelligent advice to developers as they type codes into a text editor or IDE(Integrated Development Environment). It uses deep learning to improve the quality of coding and save time.
Its scope ranges from suggesting methods or classes in a project to detecting syntax errors before saving your file.
Some early code completion tools include:
Dash – Intelligent code completion solution for developers that provides API through third-party services like VIM, IntelliJ, and Atom.
Lighter – It offers a 3-in-1 code generation tool for mobile developers that automatically suggests codes based on pre-defined templates.
Codota – It uses NLP to detect errors in your code before you save it.
6. Software Code Intelligence: Automated Code Review Tool & Code Snippets Recommender
AI detects problems in code using advanced algorithms to automate the code review process. It is an essential part of QA because developers spend a lot of time reviewing their work before it goes live. Many delays are caused by this manual effort, which can be avoided with assistance from AI.
It also recommends snippets for programming languages to help developers save time and complete tasks faster.
Some early AI tools include:
DivvyCI – It uses code analysis to ensure code quality and improve developers’ productivity.
Brakeman – It identifies problems in Rails and Ruby applications by performing quick audits across Ruby on Rails projects.
7. Planning, Estimation, and Tracking: Productivity Monitoring (KPI) Tools
AI-based productivity monitoring tools make it easier to schedule tasks and monitor projects. They do this by tracking developer activity and flagging deviations from the task list. AI can also identify resources that need additional attention and who’s working on what project as developers move through work.
Some early tools for productivity monitoring include:
HackerTracker- It uses machine learning to find out which programmer is the best.
DashThis- It offers real-time data on developer activities with just a few clicks.
8. User Interface for Applications: Chatbots & Virtual Agents
AI assists developers in building chatbots that work like real people. It does this by improving the user interface behaviour so that users don’t feel detached from their interactions with chatbots.
Some early tools for virtual assistance include:
Motion AI: It offers an easy way to build personalized chatbots for mobile apps within hours.
Famous Bot: It uses neural network technology to improve conversation capability.
9. Informed Decision Making: Advanced Analytics Tools
AI provides accurate insights into your software product development processes, which helps you make better decisions. It does this by looking at historical data on code review problems and their root causes, and how developers work during the day.
The technology can also predict things like product quality and resource allocation. It allows you to identify focus areas by prioritizing problems that reduce productivity or impact user experience.
Some early tools for making informed decisions include:
Magic – It uses predictive analytics to review code, manage performance, and track the software team’s progress toward goals.
Insightful Design – It combines data visualization with machine learning to find out how developers work.
10. Accurate Project Estimate: AI-Assisted Planning & Scheduling
Manually scheduling tasks for software projects is time-consuming and prone to human error, which is why you need an intelligent assistant to help create a flexible timeline that can adapt to changes in the project.
Some early tools for automatic scheduling include:
Gantter: It helps you map out and manage your projects.
SmartDraw: It offers a quick and easy way to create diagrams and charts without learning any complicated software.
Conclusion: The Future of Software Development Is AI-Assisted Programming
The increasing number of AI solutions available for software development shows that the technology is advancing rapidly. The goal now isn’t just about automating repetitive tasks but entire processes so developers can focus on more creative efforts.
It’s also worth noting that many of these tools are already available for you to use, which means you can start using AI today to simplify your work and boost productivity.
The future of software development is AI-assisted programming, and the sooner you embrace this reality, the better off you’ll be. So, go ahead and hire an AI developer to help you solve some of your problems. They will show you how the technology works and give you a demonstration of the final product.
