How to Start Using AI in Software Development
Richard Baldwin, an economist, once said - "It's not AI that is going to take your job, but someone who knows how to use AI might." AI or Artificial intelligence has been in use for a long time. With the coming of ChatGPT in public, the use case of AI has broadened beyond imagination. Now we know it uses GPT-4 to respond to our queries, making the entire scene more interesting. It all started with using ChatGPT for creating content or research. However, with folks maximizing its usage potential, it has successfully turned itself into a capable AI in the software development space.
AI is a machine with conversing and understanding qualities like a human. Most of the AI's intended use is to improve process efficiency. With AI being empowered with the intelligence of humans, they are capable of planning, learning, manipulating, and organizing! Furthermore, AI solutions that are integrated with deep learning and machine learning enable them to learn human intelligence and respond accordingly.
Be it for creating your favorite sports car or for designing a marketing campaign, AI is leading the workforce efficiently. Every industry has been using AI; however, the use cases of each industry vary immensely. There is a wide variety of software that developers work on throughout the year. From SaaS solutions to smartphone applications, everything falls under the software development category.
According to Statista, global expenditure on enterprise software applications is growing yearly with an average of 10%. In 2023, the spending is around $ 856.03 billion, which is nearly a 10% growth over $ 783.46 billion spent in 2022. Additionally, according to the spectacular trend of AI globally, the market is expected to reach $2,025.12 billion by 2030 from $515.31 billion in 2023.
While everyone feared the industry might see a fall in demand during and after the COVID-19 pandemic, it didn't! Instead, the growth has been phenomenal. It shows the ever-increasing demand for software development in the market. Here, AI in software development can make a huge difference. It will alter the way developers work on their assigned roles.
In this article, you will understand how we can utilize AI in software development. We will also cover information on the impact of AI in software development and the trends in the future.
The Software Development Industry – An Overview
In software development, building software from scratch covers several steps. Right from conceptualization to the final product, the entire process has a step-wise approach. This structured process is called the software development life cycle (SDLC).
AI in software development can be used in the different stages of development. It will deal with certain mundane tasks that can be automated. At the same time, developers can work on things that require their expertise and skills.
AI In Software Development – How To Start Using It?
Platform Thinking Instead Of Design Thinking
The evolution of thinking from the design perspective to thinking from a platform perspective happens with AI at the core of the development process. One key thing to remember is that evolution and exploration should be the core aim of an AI-driven process. For software design, the goal has been outcome-oriented for years, but with AI, this changes to goal-oriented. With AI in software development, the team will be able to
- Quickly build a functional proof of concept and not just non-functional design prototypes
- Conduct A/B tests & multivariate tests with real end users
- Deploy fully tested applications based on real-time user evidence
As AI will assist professionals in design and delivery, the team will organically inherit platform thinking. Ultimately, the entire team will be able to visualize the end-user experience. Based on the same, they can alter or modify the platform seamlessly. In this process, where the entire team is activated regarding platform thinking, the development becomes agile and accurate.
When AI in software development becomes a regular practice, teams must evolve new skill sets to utilize AI. AI engine-powered bots, analytics, reporting, and suggestions will take over the entire software delivery team.
AI in Software Quality and Testing Process
In the traditional SDLC, quality measures and testing take much time for the developers. Even then, there are instances when specific errors slip the human eye. It leads to visible bugs once the application is made live for the users.
As AI becomes a functional tool, it can play a crucial part in the quality assurance testing process. With AI, the process of testing and quality checks will become a lot faster. It will eventually reduce your delivery time of high-performing software solutions for the clients.
Such a testing process requires the developers to go through truckloads of data. With AI in use, they can focus on other parts of the SDLC while AI handles data.
Whether you are a QA professional, developer or startup owner willing to try out AI software testing, here's a list of the top 3 AI testing tools.
TestCraft
TestCraft allows companies to run codeless and automated testing to deliver web apps with agility, scale and top-notch quality standards. TestCraft is known for offering Selenium-based automated testing. Here are the two ways it stands out:
- Teams are allowed to create tests that can run repeatedly sans coding
- Reduce maintenance massively using TestCraft's adaptive AI/ML technology.
Key Features
- AI-driven test idea generation
- Automated test creation
- Accessibility checks
- Generate test scripts from test ideas
Pricing
Depending on your use, there are a range of subscription packages on TestCraft. The packages vary from trial, monthly and yearly plans. The entry-level pricing for a user starts from $29.
Functionize
Functionize is one of the most popular cloud-based automated testing techs in recent times. This tool helps expedite test creation, maintenance and diagnosis by employing machine learning and AI. The user-friendly approach to testing makes it stand out from a host of other AI-powered testing solutions. Forming test cases is super easy as it allows typing the input in plain English, which is then processed by natural language processing (NLP). It effortlessly generates over a hundred test cases in minutes!
Key Features
- Fast test case creation
- AI-powered maintenance
- Quick debugging and easy edits
- Adaptive Scalability
Pricing
Functionize offers a quotation-based pricing model for the users. You can select the specific features you require, based on which Functionize will provide you with a quotation.
AccelQ
When speaking of AI-driven codeless tools, AccelQ is a must-try! It empowers multi-channel test automation across mobile, desktop, API and backend domains. It helps in continuous test automation while integrating business process-driven automation across all the tech stacks.
Key Features
- Swift test automation
- Unified automation for desktop, API and mobile
- Insprint automation for DevOps
- Use of visual application model
- AI-generated test cases
Pricing
Users can either choose the free version or use a paid version based on their requirements. A user is subject to a free trial period for every paid version!
Reduce Coding Time
If you ask, will AI replace software developers? The answer would be "no"! It will take enough time from now for AI to be completely able to write code. But some tools will assist developers in their development gigs.
AI-powered software such as GitHub Copilot, Amazon CodeWhisperer and OpenAI Codex help reduce developers' coding time. These tools suggest codes and complete the codes when developers are writing them. This is similar to the autocorrect feature folks use while texting on mobile phones. That's how simple is the usability of such AI-powered tools in software development.
These AI-powered tools are helping developers finish gigs as fast as possible with utmost efficiency. The purpose of these tools is not only to assist pro developers. They are designed to educate budding developers as well.
GitHub Copilot
https://github.com/features/copilot
GitHub works in collaboration with Open AI to bring GitHub Copilot. It aims to change the coding experience using advanced features and abilities. The use of AI and ML ability, it enhances a developer's coding efficiency.
Key Features
- Popular IDE integrations
- Multiple programming language support
- Code suggestions and function generator
- Simple auto-complete navigation
Pricing
GitHub Copilot for individuals starts at $10 monthly, while for businesses, it starts from $19 monthly. You can choose yearly plans to avail substantial discount on the final price.
Amazon CodeWhisperer
https://aws.amazon.com/codewhisperer/
Amazon CodeWhisperer is known for enhancing a developer's coding accuracy and speed. It adds more precision and speed to the process of writing code. Powered by Amazon's AI technology, it can suggest code, generate documentation, complete functions and more!
Key Features
- Function completion
- Code Suggestion
- Security Scanning
- Multiple programming language support
- Supports integration with varying IDEs
Price
Individual developers can use Amazon CodeWhisperer for free. For business/professionals, the service is based on a "per user per month" basis. It is priced at $19/month per user.
OpenAI Codex
https://openai.com/blog/openai-codex
The one thing that makes this tool a favorite in the market is its quick setup ability. AI code completion and natural language processing prompts make life easier for developers. Using plain English instructions, the developers can write code seamlessly!
Key Features
- AI Code Completion tool
- Quick and easy setup
- Natural language prompts
Pricing
Here comes the ultimate good news! OpenAi Codex is a completely free tool for developers. Get on the platform and use it for free to generate any code you want!
Manage Bugs and Errors Efficiently
Upon initiating an AI-driven software development process, AI can do another major work for you: manage and reduce bugs. Most software have some or the other error/bug that the developers miss out on.
With AI to detect errors, the process becomes faster and more efficient. When AI detects any error, it will fix the issue almost immediately. The AI-powered process is so efficient that it doesn't require a developer!
Predictive analysis tools powered by AI take a look at the database to suggest changes for the future. Such suggestions help developers mitigate bugs that might arrive with time and more traffic on the platform.
Enable Real-time Feedback
User feedback is a top priority for every developer. It assists majorly in terms of adding improvements to the final product/software. AI assistants for software developers can help gather real-time feedback to enhance user experience. With AI and ML leading the innovation, they can learn the user's behavior and interaction patterns. Such data is further analyzed and presented to the developers. The developer can fix any performance issue on the platform with the help of such data.
Engage AI in Strategic Decision Making
AI can automate strategic decision-making, reducing the need for human interaction. The time spent debating possible solutions to a problem or adding new features by developers is quite long. AI can radicalize the division-making scenario by reducing time for debates and discussion. AI shall utilize data and analyze it to understand what can be the right approach to a work. If the AI has been used previously in the same work for any other software development, it means it is trained for the gig.
The core requirement while making any strategic decision is that it needs to be backed with enough data. AI assistants for software developers use detailed analytics from the data to make a decision. As the power of computers and digital storage space increases exponentially, AI agents will be closer to human intelligence and offer smarter decisions.
Another significant benefit of AI in software development is reducing human errors and biases. Every decision has enough data in its support, which machine learning technology analyzes. It helps to make intelligent and informed decisions in the software development process.
AI for Estimates
Every client or software development company faces a major issue - they lack a proper estimate for any proposed project. With the AI software development process gaining momentum, there is also a solution to this. AI trained on past projects will possess knowledge and understanding of the entire workflow required. It can use predictive analysis and recall past data to give accurate estimates in terms of pricing, team strength needed, and timelines of deliverables.
AI allows the team to get an idea of the possible roadblocks in a particular project down the line. It also gives an idea of how much delay will happen due to such challenges in the software development process. With this information, a software development company can decide whether to take up the project. The accuracy of the AI assistant for software developers in providing correct estimates further helps build trust among clients. It adds credibility to the business and boosts client retention rates.
Now that you know how to start using AI in software development, let's look at the challenges that come with it.
Challenges Of Using AI In Software Development
Low-quality data
The core concept of using AI is that it uses data to understand and learn every process. The challenge here is the lack of good data and qualified information. If the AI receives poor quality data and that too in low quantity, its decisions will be downright bizarre. AI cannot logically understand which data is worth it and which is not. It negatively impacts the accuracy and performance of the AI assistant for software developers.
No Transparency
The AI models are complex, making it challenging for developers to understand the basis on which they make any particular decision. Because of this lack of transparency makes recommendations on bugs and errors challenging to maintain and integrate into the software.
Ethical Considerations
The use of AI in software development will keep growing in the years to come. However, ethical considerations on issues such as privacy, transparency, and biases have already started to crop up. The top AI software development companies, need to figure out a way with tech to deal with this concern.
Human Acceptance
Now, this is truly a concern that will take quite some time to be totally resolved. As AI keeps improving with such work, the question that will crop up is whether AI will replace software developers? By now, it is clear that AI cannot replace human developers. But just like in any other profession, even developers will try hard to resist AI in their industry. Whatever work AI can do is now being done by human developers. Hence, there will definitely be job cuts with AI's mass-scale adoption in software development. In the long run, sorting out this considerable challenge will require much time and initiative.
Cost, Computational Power, and Resources
When we consider using AI in software development, the cost of developing and deploying such a solution is high. Plus, it needs a specialized skill set to create the hardware, software, and infrastructure for an AI application capable of dealing with such complex tasks.
Another challenge is the need for high-end computational power and systems to operate these AI tools. For smaller organizations and individual developers, it becomes difficult to afford such a massive setup to support these AI tools for development.
Best AI Development Software And Tools
A host of AI development software and tools can help developers in their work. Here are some of the most popular ones-
- PyTorch – Pytorch is one of the most popularly used open-source machine learning frameworks. The main use of the platform is for the training and development of neural networks.
- TensorFlow – When it comes to the development and deployment of deep learning models, TensorFlow enters the scene!
- SciKit-Learn – SciKit-Learn is one of Python's most efficient and robust machine-learning libraries. The Python consistency interface provides efficient tools that help in statistical modeling and machine learning. Dimensionality reduction, clustering, regression and classification, all of these get a proper boost with the use of Sci-Kit-Learn.
- H20.ai – H20.ai is of utmost help if you are looking for an open-source platform to build and deploy top-notch machine learning models. You get multiple algorithm support, and H20.ai also easily handles large datasets.
- Microsoft Cognitive Toolkit – If training and deploying massive deep neural networks is your ultimate goal, this is your solution! Microsoft Cognitive Toolkit is an advanced deep-learning framework. It is enabled to deploy such massive deep neural networks across numerous GPUs and systems.
Tips for Software Developers to Stay Relevant in this Age of AI
As AI in software development keeps gaining popularity, it will be necessary for developers to upskill. They must focus on learning new trends and technologies to stay relevant in this fast-evolving software development market.
Here's what developers can do:
- Utilize the time to learn more about trending technologies. Artificial intelligence, machine learning, and computer vision should be at the top of your list!
- Master new tools in the market that use AI technology to foster the software development process.
- Focus on improving essential soft skills, which include communication, problem-solving, and people management.
- Skills such as data science, statistics and cloud computing are trending in the market. Learn more about these and establish your expertise with advanced usage of these skills in the software development process.
Conclusion
The bottom line is that AI is going to be an invaluable addition to almost every business process within the next few years. AI enhances the efficiency and pace of the software development process. It is set to take center stage in the years to come. AI-powered chatbots, decision-making tools, or AI assistants will soon be integral to every process. The software development industry is evolving at a rapid pace. Adopting new technologies promptly and efficiently is suggested to stay ahead of the competitive curve.
As discussed in this article, AI's benefit is not limited to software development. The core reason why AI is beneficial in any process is that it reduces human effort while increasing efficient productivity. And it is crucial to understand that time is the key element for any DevOps organization in this highly competitive landscape. When coders, testers, and project managers learn how to utilize and reap benefits from AI, the company's overall productivity shoots up!
