Software developers and businesses using Artificial Intelligence (AI) during and after the software development process are leveraging AI-driven decision-making, optimised performance, and enhanced productivity. Some businesses and developers are using machine learning algorithms to learn, adapt, and improve on the move.
Regardless, AI tools have become integral to software development. AI statistics and trends show that 77% of all devices used today have some form of artificial intelligence, while 77% of businesses consider leveraging AI-powered tools in their businesses, with 63% of the companies aiming to adopt AI in the next three years.
Discover how to use AI in software development to achieve a competitive edge, optimise software quality, and make data-driven decisions.
Some Benefits of AI in Software Development
The benefits of AI in the software development process exceed automating routine tasks by incorporating AI tools. Artificial intelligence can streamline multiple processes in software development, enhance code quality, and empower next-level development team performance.
Streamlined Software Development Process
AI tools help developers by automating repetitive tasks in the software development process. Routine tasks can become time-consuming, while AI-driven training data or models to automate them allow developers to focus on quality over speed. AI-assisted coding speeds up the process without compromising the quality of coding tasks because the human touch remains during the reviews.
Enhanced Team Performance
AI systems designed to automate coding, testing, and other coding tasks can also help developers work harder, not just faster. For example, machine learning models make sure developers write code that meets specific quality standards outlined in the training data, which allows developers to tap into their own problem-solving abilities, creativity, and productivity while AI handles mundane tasks.
Real-Time Decision-Making
Some AI tools enhance decision-making among developers when working on complex software solutions, empowering them with valuable insights using real-time analytics. Artificial intelligence is able to process the software code quality far quicker than human developers, and machine learning models compare the code to high-quality criteria that empowers developers to make fast, efficient decisions.
Faster Coding Process
Software projects that implement AI coding tools also have a faster time to market because developers don’t have to focus on the time-consuming coding process. AI generates code snippets developers can review with tools like GitHub Copilot and others, making sure the software development process prioritises speed and code quality simultaneously.
Complete Guide on How to Use AI in Software Development
Creating high-quality software solutions with AI tools is possible. Applying AI-powered code in software development improves many processes, but here is how developers use AI during the development process to deliver excellence in every line and function:
Project Management
Some AI platforms are designed to streamline project management, including resource allocation for coding tasks, team communications, client updates, and iterative stage development. Developers and project managers benefit when AI-driven project management programs simplify time-consuming tasks.
Developers don’t use AI systems for code suggestions alone. Programs like Forecast, Motion, Wrike, and Time Hero can analyse data to streamline task scheduling during iterative stages of development, manage risk based on the data, and handle resource allocation tasks based on input data.
Prototyping
Recent research on how SMEs use AI in new product development found that they used AI systems in 13 applications to improve the process and they boosted five key performance indicators. Developers implementing AI models for code suggestions prototype solutions faster and more efficiently.
AI-powered prototyping doesn’t replace human developers. They simply use AI tools and machine learning algorithms to prototype products and software quickly to gather feedback and make further improvements before developing the final solution that will meet market demands.
Code Generation
Some developers use programs like Cursor, GitHub Copilot, WindSurf, and Aider for AI-generated code snippets while others use generative AI programs for high-quality code generation from start to finish. The developers still review the code to make sure it meets the documented requirements.
However, code generation using generative AI platforms can automate repetitive tasks that would delay development. It can also improve problem-solving when software engineers work on complex software projects that require careful planning and code structuring.
Code Completion
It takes professional and job-ready software engineers a long time to learn coding, with the average developer taking 12-18 months to master the most common languages. AI system completion strategies ensure a human touch with higher software quality from entry-level engineers.
Senior software engineers also use AI platforms to complete code in complex projects before manually reviewing it or using advanced programs to refactor the snippets. Generative AI platforms like Gemini, Chat GPT, and Bing Copilot easily grasp complex ideas to problem-solve for code completion.
Software Documentation
AI-driven software development also leverages AI to document the requirements and specifications for software solutions comprehensively and thoroughly, encouraging developers to focus on the product and not writing extensive documents that streamline maintenance after the launch.
Comprehensive software requirements specification (SRS) documents are integral to developers writing high-quality code and maintaining the systems after clients receive them. It’s also essential to have the documents for existing code to make sure future maintenance and upgrades are seamless.
Code Reviews and Testing
AI-driven programs like Testim and Applitools empowers developers to automate the creation, execution, and continuous improvements in test cases. AI algorithms conduct code reviews, identify edge cases, predict potential failures, and expand testing coverage for high-level software projects.
AI software using machine learning algorithms help developers debug software and analyse runtime errors much faster than using outdated tools and testing platforms. Software testing is important to ensure quality, integrity, and reliability. These tools even make suggestions that improve the software.
Code Optimisation and Refactoring
AI-powered software development ensures that programmers write code that exceeds expectations. Software engineers use AI models and algorithms to analyse vast amounts of code to identify patterns and inefficiencies before making code refactoring suggestions to improve software quality.
AI platforms like Sourcery integrate algorithms to manage repetitive tasks that may become mundane enough to make software engineers miss something important. These tools also have problem-solving capabilities beyond most human minds to deliver optimised software solutions.
Predictive Analysis
Software engineers also use predictive analytics for AI-powered development to improve the quality and meet market demands, much like major companies use predictive analytics and machine learning to process massive data sets that provide insights into what consumers want.
Software developers use the training data available from AI algorithms to forecast requirement changes, market demands, and other insights that could improve the software product. They also use these AI-powered tools to help clients understand software, internal needs, and market gaps better.
Natural Language Processing
Using natural language processing in software development is another core area because software engineers use AI tools with natural language capabilities to interact better with the code. These AI tools help developers make software code more intuitive and accessible.
For instance, developers use natural language descriptions to easily create code snippets without all the technical jargon. The natural processing capabilities also enhances code refactoring by making suggestions after analysing the intent behind the code developers wrote.
AI-Powered UX/UI Design
Intuitive, dynamic, and responsive UX/UI designs are integral to a successful software development life-cycle, ensuring that applications meet user needs. While AI can enhance team performance when designing the ultimate user interface and experience, it can also automate it.
Some AI systems like Uizard can analyse design preferences and interaction data before making suggestions that help developers create prototypes, wireframes, and initial drafts quickly and easily. Designers can focus on being creative while AI handles the routine tasks.
Legacy Code Maintenance
Software engineers streamline the maintenance of existing code using platforms like Snyk and DeepCode, which allows developers to leverage machine learning systems with vast access to training data analyse the software for bugs, inefficiencies, and vulnerabilities.
Snyk provides next-level security with human assessors supporting multiple machine learning types of models with generative and symbolic capabilities. AI-driven maintenance is another way AI can support software development, even after the process is complete.
AI in Software Development Pros and Cons
Implementing AI in development has some advantages and disadvantages. AI-driven software projects still require some knowledge about programming languages when implementing AI tools. Here are the pros and cons of relying on AI to develop software:
Integrating AI in Development Pros
- Software engineers are more productive during code development
- Software development projects hit the market faster with automated routine tasks
- You have top-quality code when combining the human element with AI
- Software engineers make data-driven decisions before they writing anything
- Software cost less when prompting AI algorithms to complete systems
Integrating AI in Development Cons
- It can cost a lot to initially upgrade the skills of software engineers to use AI in development
- Software development implementing AI-driven code comes with privacy and security concerns
- There are ethical considerations and possible biases when implementing AI in the development process
List of AI Tools Designed to Streamline Projects
Software engineers are using a multitude of AI-coding tools and AI algorithms to enhance the development of software applications. Some AI tools are open-source, which can also reduce the cost of developing software and mobile applications. Here are some of the popular AI-driven tools commonly used:
- GitHub Copilot enables software developers to write code snippets based on natural language descriptions. It’s commonly used for high-quality code completion for software applications. The tool can use programming languages like Python, Ruby, JavaScript, PHP, C#, Swift, and Go.
- Faux Pilot is an AI-assisted coding tool developers use for code reviews, completions, and refactoring. The AI-powered assistant can be used online or offline. The program uses more niche programming languages beyond Python, including Dockerfile and Shell.
- Bito is another open-source tool using machine learning and AI algorithms to analyse code and make suggestions that improve the quality and performance of software. Bito can create code with the Python, Java, and JavaScript languages.
- Amazon Code Whisperer is an automated AI cloud platform that makes code suggestions after conducting a review to ensure software engineers deliver the best quality. The services also integrate perfectly with AWS APIs like AWS Lambda, Amazon S3, and Amazon EC2.
- Codeium helps programmers write code more efficiently and quickly. It uses in-house generative models that allow access to over 70 languages and includes features like AI-driven chats and intelligent search. It’s an ideal program to try when creating complex software.
- Applitools is an open-source development program that allows developers to automate testing and code reviews. It uses machine learning to automate and execute different test cases. The tool can analyse languages like Python, Ruby, JavaScript, Java, C#, and Cypress.
- Snyk is a security-focused AI platform that enables developers to automate tests, check for security vulnerabilities, and make patch improvements that meet cybersecurity standards. Snyk supports languages like Go, Java, Kotlin, JavaScript, Apex, C++, C#, and Python.
- Chat GPT and Gemini are two major open-source AI-powered tools used for complex software problem-solving because these large language models have access to a ton of trained data. These programs can also streamline resource allocation and project management or planning.
How to Use AI in Software Development Conclusion
Software engineers deploy AI-powered code to streamline decision-making, speed up the time to market by automating routine tasks, analyse data in real-time for better decision-making, and enhance the code quality with AI-coding tools that have natural language processing capabilities.
Using artificial intelligence for a range of use cases throughout the software development life-cycle helps programmers deliver excellence, whether using them for code reviews, the actual coding process, or the automation of multiple repetitive tasks that often cause project delays.
AI in Software Development FAQs
What are the limitations of AI in software development?
The limitations of AI algorithms include the potential for bias based, which depends on the training data used in AI-powered tools. Data without diversity or which doesn’t accurately portray the real world could taint AI development projects.
Artificial intelligence (AI) can also possibly perpetuate existing biases, which could negatively impact code reviews or generation. Another common AI limitation is the potential for data dependency. AI algorithms need tons of data to make accurate forecasts, which isn’t always available to the program used.
The complexity of algorithms also goes over some programmers’ heads. The leading programmers that implement AI for development have a deep understanding of complex mathematical and statistical concepts. Finally, some AI programs battle to find unusual edge cases and scenarios.
They may not detect unforeseen edge cases or identify unusual scenarios that deviate far from the initially trained data. Good development teams anticipate these challenges, know which tools overcome them, and add the human element that bypasses ethical and bias concerns.
How can I avoid ethical issues when creating software with AI algorithms?
AI depends on large data sets trained with tons of personal data, which leads to some ethical concerns. The concerns are about data privacy, the misuse of personal information, and a lack of consent. Software engineers wanting to avoid these issues should use access controls, encryption, anonymization, and other strict safeguards to protect personal data when developing with AI.
It’s also ideal to follow AI development best practices and implement some form of accountability for AI decisions. Make sure software complies with legal and ethical standards set out by the General Data Protection Regulations (GDPR) and European Union’s Artificial Intelligence Act. Meeting both regulatory standards and guidelines will prevent ethical issues in AI-developed software.
What are the best practices for developers that use AI in product development?
The ethical concerns encourage developers to focus on implementing best practices when integrating AI into software, automating repetitive tasks, or using artificial intelligence during any part of the software development life-cycle. Here are the industry-leading practices every programmer must ensure to meet the ethical standards for artificial intelligence in software:
- Carefully and comprehensively document all use cases for artificial intelligence in development.
- Align the artificial intelligence systems with your internal practices for software development.
- Create specific, unique, and well-detailed prompts before coding with AI products or assistants.
- Manually review and test code when using artificial intelligence in any stage.
- Seek expert-level validation from another programmer before launching AI software.
- Follow the Agile or any other iterative development process when integrating AI into software.
- Design and implement a step-by-step prompt workflow that can be followed and documented.
- Restrict access with multi-factor authentication to prevent any unauthorised access on products.
- Guard the software project against prompt injections as necessary in other generative projects.
- Don’t allow artificial intelligence to handle API keys unattended when relying on AI assistants.
- Review the library suggestions made by artificial intelligence with a fine-tooth comb.
