In the first half of 2023, we have seen artificial intelligence, deep learning, and generative models making headlines by transforming the way businesses operate. The world has known AI mainly for its applications in research and data analytics, but it wasn’t really much of a hot topic until the emergence of ChatGPT. AI is now in the spotlight as it promises to ease the processes involved in software development, content creation, advertising, and numerous other fields.
From laying out website structures to generating meaningful code, we could not have guessed until a few years ago how AI will change software development and applications. The role of AI in software development is far greater than just streamlining the overall process. With such data processing power, you can improve your planning and research by a significant margin. Of course, you are able to write better code, but the real magic happens when AI enhances the speed and accuracy of automated tasks.
That brings us to the question everyone is asking - how to use AI in software development?
Given the availability of modern AI and ML tools, there is no doubt that businesses need to ditch traditional software development practices and rely more on automation. This blog post covers the best practices that help designers and engineers leverage AI in software development. But first, let’s see what AI can do in software development.
Understanding AI in Software Development
One of the first and most significant benefits of AI in software development comes in the form of code analysis. Every now and then, software developers struggle to manually keep track of code changes and test results. But with AI by your side, you get in-depth analysis and helpful insights related to software quality, performance, and potential bugs.
As a result, you are able to streamline your software development process and also improve the quality of your end product.
The constantly evolving field of AI brings about new benefits of AI in software development every other day. For starters, GPT tools help developers generate code snippets and even complete the code for them. Then, using predictive analytics, AI allows you to identify patterns in code to provide estimates to clients related to project completion time. You can even optimize your code and automate the lengthy and hectic process of documentation.
The Impact of AI on Software Development
As AI is continuously evolving, it would not be justified to define its impact on software development or any other field just yet. However, it is safe to say that AI has helped reduce the time-to-market by improving efficiency and accuracy in every single step of the software development process. For instance, all software development life cycles begin with ideation, requirements gathering, and research.
Here, AI helps you by providing valuable insights about previously developed software in a similar category, the technologies and frameworks used to build them, and the challenges associated with them. And that’s just the start of it. Whether you consider code generation, testing, or quality assurance, AI has its applications everywhere.
Streamlining Development Processes
Some of the processes in your software development life cycle are repetitive, time-consuming, and laborious. With the help of complex machine learning algorithms that constantly learn from the data they process, AI analyzes structured and unstructured pieces of code to automate repetitive tasks. So if you’ve been wondering how AI will change software development and applications - the answer is through automation.
Developers can use intelligent suggestions provided by AI tools to complete their code, detect bugs, and also automate their software testing process. Here are some of the AI-powered tools and frameworks being used in software development as of 2023:
Google’s open-source AI framework TensorFlow helps developers create machine learning and deep learning models. It has a wide range of tools and libraries to assist developers in areas like natural language processing, image recognition, and numerical computations.
AutoML is another great tool from Google that is designed to assist programmers with limited experience in training machine learning models. The vision behind this AI tool is to help train ML models with ease. Using a simple graphical interface, developers can choose training objectives from a built-in library. The data for creating custom ML models is categorized into three separate sections - training, validation, and test sets.
Machine learning is a tricky area of software development, which is why it is crucial o manage the different machine learning models created during a given project. Here, AI tools help ensure that the ML models you have trained continue to learn from the data they process and improve over time. Some of the notable machine learning lifecycle management tools are as follows:
Amazon SageMaker - a comprehensive machine learning and deep learning tool that provides integrated development features.
Azure Machine Learning - Microsoft’s cloud-based tool for keeping track of ML models.
Google Cloud AI Platform - for those using TensorFlow and AutoML, Google Cloud AI is the perfect combination of tools to streamline workflow and also track ML models.
Enhancing Software Testing and Quality Assurance
Every software needs to be tested and taken through quality checks before it can be deployed. Coming back to the topic at hand - how to use AI in software development, it is important to address the crucial step of testing and quality assurance. Recently introduced AI techniques like static code analysis and automated test execution have significantly improved the process of software testing.
An AI-based testing system takes the software through different code paths with a variety of input combinations. Using innovative techniques like symbolic execution, model-based testing, and fuzzing, AI creates unique test cases automatically. It then runs these tests automatically and generates results containing, failure patterns, anomalies, and critical issues.
AI detects vulnerabilities and inconsistencies in the code through machine learning algorithms, saving you time and resources. Moreover, AI can also enable you to test the performance of your software by running simulations of high user volumes and stress testing.
Enabling Intelligent Applications
There are various stages involved in creating intelligent software applications. Firstly, an AI model is taken through a training phase where it is provided with information to ‘learn’. This is made possible by providing possible inputs and their corresponding outputs. By conducting data analysis, the AI model uses its built-in programs in the right ways to achieve results as close to predictive outputs as possible.
The next step involves feature extraction where the model picks out patterns from inputs and identifies the relevant features that can help generate the right output. After that, the AI models are constantly optimized to reduce the possibility of errors. All AI-based applications incorporate a feedback loop, where the model constantly stores and learns from the inputs it receives.
As with most software solutions, feedback is generally submitted by users either in words or through ratings. AI models use these ratings to learn about customer expectations and to improve their decision-making for future outputs.
Integration of Machine Learning in Software Applications
The benefits of AI in software development come full circle when you look at the applications of machine learning. Starting from predictive analytics for identifying patterns and generating valuable insights, and all the way to anomaly detection, machine learning really has a lot to offer.
Analyzing the normal functionality of software and being able to identify outliers allows the system to detect and report all kinds of possible issues such as fraud, network intrusion, or hardware failures.
Machine learning applications do not just stop there either, you also have pattern recognition that ultimately helps build accurate voice and gesture recognition systems.
Apart from tracking the performance of software systems, AI is also beneficial in collecting and analyzing user data. Information gathered from user journeys allows software service providers to generate personalized recommendations in terms of content, advertisements, and suggestions.
How to Use AI in Software Development
Now that we have discussed the theory of it all, let’s get down to business. While the benefits of AI in software development are evident, it’s important to understand how to use the relatively new technology. With a variety of AI tools available in the market, and more being introduced every other day, developers have a hard time making their selection. The tools, algorithms, libraries, and frameworks you choose must align with the coding language you are using.
AI-Powered Development Tools
AI-based tools come in various types as their purpose is to address the challenges faced in each stage of software development. First of all, you need to have an AI-based IDE (integrated development environment) or code editor. Some popular examples include Visual Studio IntelliCode, Kite, and Tabnine.
These solutions provide software developers with code suggestions based on patterns from codebases processed earlier. Kite is an AI-powered development tool that goes one step ahead by writing documentation and providing context-specific recommendations.
Implementing AI Algorithms
While having all these AI tools and algorithms sounds exciting, implementing them in the right way is the tricky part. If you go for the traditional approach, where you have to follow a rule-based system with predefined logic. This works when you are building an application with definitive rules and conditions, but not where you are required to create something out of the box.
The next approach is through machine learning algorithms, where the system can perform tasks without being given commands and learn patterns over time. Initially, you need to provide the system with labeled data for its learning. Neural networks and regression trees are common examples of machine learning algorithms in software development.
Examples of AI Algorithms in Software Development
Natural Language Processing (NLP): Starting with an example that has introduced ChatGPT to the world, AI algorithms are implemented in software development to make them understand natural language.
Image Recognition: From smartphones to autonomous vehicles, there is a wide range of applications for convolutional neural networks (CNNs). AI algorithms help detect objects in images and also automatically categorize images based on predefined criteria.
Recommendation Engines: It’s always great to see content on websites based on past searches and other online activity. But what goes behind generating these personalized recommendations? The answer is AI algorithms. With content-based filtering and collaborative filtering, AI algorithms simply leverage user preferences, searches, and preferences within applications to suggest highly relevant content.
Leveraging AI Libraries and Frameworks
Libraries and frameworks are a crucial part of a software developer’s life. In order to implement AI algorithms the right way, you need to have the best AI libraries and frameworks. Here are some popular choices that help accelerate and improve the quality of software development.
Microsoft Cognitive Toolkit (CNTK): A scalable deep learning library that trains AI models for image, speech, and text processing.
XGBoost: When it comes to solving structured data issues, XGBoost is the open-source library for you.
Apache Mahout: A platform used to develop machine learning models and algorithms for recommendations, classification, and clustering.
Based on your specific requirements, AI libraries play an important role in providing you with the right resources to develop the right software.
“Programming trends suggest that software development will undergo a radical change in the future: the combination of machine learning, artificial intelligence, natural language processing, and code generation technologies will improve in such a way that machines, instead of humans, will write most of their own code by 2040,” said the research team.
With deep learning already being used in numerous applications, we can expect reinforcement learning to take things to the next level. Reinforcement learning is like machine learning on steroids - it can perform more complicated tasks with lesser learning time through data abstraction.
And how can we forget what’s already here? Generative models like ChatGPT obviously need no introduction. It remembers the results of your previous commands and simply carries on from there - just like humans.
The world of software developers has definitely been shook by the developments in AI. According to Evans Data Corporation, a California-based research firm, software developers now feel that AI will replace their development practices in the near future.
With respect to software development, one of the best code analysis services available today is SonarCloud. It is trained to identify coding issues and works for 26 different programming languages.
With the exponential growth of AI, we will soon see the rise of cloud-based software development as more and more companies shift to the cloud. As software developers look for ways to improve user experience, AI will help in building user-friendly applications based on user feedback, predictive analytics, and so much more. If the goal is to innovate and create an impact, then there is no doubt that AI is the future.
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