Artificial Intelligence (AI) has revolutionized many facets of life as we know it. From your daily commute to browsing your email inbox, there is a bit of AI in almost everything these days, and the industry is not yet at full throttle.
According to reports, the AI market will grow to more than $118 billion by 2025 from around $10billion in 2018. Since AI is impacting various industries, it is only natural that the software development industry will not be left behind.
Every day, software developers face complex challenges from a wide range of clientele looking for specific solutions. To address the pain points of the various clients and deliver solutions that work without fail, developers are enlisting the help of AI.
Artificial Intelligence is an excellent fit for software development because it can understand, replicate and improve on human behavior. This simplifies the software development process.
This piece focuses on ways artificial intelligence is improving the software development industry.
Table of Contents
- Enhanced Ideation
- Better Software Testing and Quality Assurance
- GUI Testing
- Automated Bug Fixing
- Higher Quality Decision-Making
- Automatic Code Generation
- Accurate Project Completion Estimates
- Improved Customer Service Delivery
Businesses around the world are always in need of software solutions that can improve operations. Some enterprises run entirely on software. To get the solution they need, they have to find competent software developers and communicate their needs.
Unfortunately, it is not always easy to understand the requirements at first glance. Even when the software needs are precise, turning the idea into code can prove challenging.
AI has simplified this problem. Today, software developers can feed natural language to an AI system to generate ideas. The AI turns approved ideas into executable code quickly allowing the developers to only focus on putting the finishing touches.
This means that many developers now have AI systems that function as partners. These “partners” show the developers the best way to approach a specific project.
Some software development solutions have taken this a step further by creating platforms that allow you to feed ideas to an AI system, and then receive a completed solution.
The Artificial Intelligence Design Assistant by Bookmark is an example here. With this AI solution, users can create a website in minutes.
Better Software Testing and Quality Assurance
Developers now use AI to test code, find flaws and fix them automatically. This has led to more deployment of higher-quality software solutions with improvements in the overall time-to-market.
Software developers no longer need to be fully hands-on in the testing process, but instead, they can now focus their attention on taking actions based on test findings.
AI ensures a more-efficient software testing process by generating actionable data while humans focus on putting the generated data to good use.
The Functionize tool, for example, features AI-enabled cloud testing that allows users to test software fast and beat release deadlines. To use this tool, all you need to do is to type a test plan in plain English, and it will be converted to a test case.
With this tool, developers can test for function and performance across multiple devices and servers. The entire process is completed in the cloud. This AI tool also features a self-healing function which allows it to develop tests in real-time.
Graphical User Interfaces (GUI) are the lifeblood of software interactions today. More critical systems rely on these interfaces to function, making regular testing necessary to avoid disruptions.
Testing GUIs is challenging as there are limited tools and techniques available for the process.
To run a GUI test today, test designers have to first manually generate test cases, pinpoint the conditions that require checking during the process of executing the test, decide on the time for checking the conditions and then run an evaluation process to determine if the GUI is sufficiently tested.
The process described is without doubts, highly demanding as every change to the GUI means a re-test. This is why the GUI testing processing is a resource-intensive one.
AI has stepped in to simplify the process with solutions like Applitools now in use. This AI-powered GUI tester automatically checks if the visual code is functional or not.
This allows developers to test the visual code with the same intensity as the functional UI code to be sure that the appearance of the software is exactly as it should be.
Applitools also allows you to see how your application looks across various screen layouts to ensure that the design is indeed cross-platform.
Some developers also use Applitools to track web page behavior making this AI solution the perfect choice for testing visual appearance and overall function.
Overall, we are still in the early days of using AI for GUI testing, but the results are promising.
Automated Bug Fixing
The amount of data processed during software testing makes it difficult to find all the bugs created during the software development process. Artificial intelligence is addressing this challenge.
With AI, developers have created tools that can find bugs. Once the bugs are isolated, the AI can either fix them automatically or send the recommendations to human engineers for final approval.
SapFix is an example of such an AI solution for bug fixes. It automatically generates fixes for bugs and then seeks approval from engineers to deploy the recommended solutions.
Higher Quality Decision-Making
Deciding what features to include in any software during development can be a long and arduous process. AI systems that are already trained on similar projects, however, can deal with this challenge.
They analyze the performance of related solutions and then recommend the best solutions to project managers and clients. The result is a software prototype that meets all the basic needs of the client.
Usually, converting the requirements of business stakeholders into actionable software specifications gulps a lot of time. With AI, however, software developers can cut down on time spent at this stage and deliver a solution faster.
This will, in turn, allow the development company to take on more projects and surpass revenue expectations. AI canvas is an example of such an AI solution.
It helps companies to find out the vital questions and feasible challenges they are likely to face when building software.
Automatic Code Generation
At its core, coding is labor-intensive. Developing a massive project from scratch can require multiple developers pitching in with thousands of coding hours individually. Fortunately, AI can reduce the workload a great deal.
In the past, developers have tried to create systems that can write code on their own to save time and money. Unfortunately, most of these methods require a lot of details as they couldn’t interpret ambiguous language.
Putting down the details in a way the systems could understand, ended up taking as much time as just creating the code manually.
AI, on the other hand, has taken developers one step closer to fully automated code generation. Solutions like Bayou, now exist. It is an AI-based programming assistant that uses neural sketch learning to find patterns in millions of Java programs.
It figures out the intent behind a development project and then creates the best possible template from the models that are most similar to the requirements of the project.
The Bayou project is designed to make programming easier and without bugs.
It is only a matter of time before we have similar AI-powered solutions allowing developers to spend more time on other aspects of running their software development businesses.
Accurate Project Completion Estimates
As software development teams work on more projects together, it is easy for them to estimate possible project completion dates.
However, external factors like code bugs and a client that keeps changing the goalposts can make it harder to stick to the projected deadlines.
AI can ensure accurate project completion estimates even with the possible external factors that can influence the development process.
The overall accuracy of the forecasts will improve further with every completed project as the AI will combine developer output statistics, predictive analysis, correlations, and the business terms and conditions to provide an estimate.
Improved Customer Service Delivery
Software development companies are not exempted from providing quality customer service to clients.
In reality, they have a lot of customer service responsibility as they are continually working with clients that may not understand the technical language but still want to be kept abreast of the development process.
Software developers can use AI to create a customer-service funnel that takes care of critical customer service processes while they focus on delivering high-quality software.
This is especially true for smaller teams. AI can play a role in managing elements like social media interactions, support, lead-follow up and more.
The use of Chatbots is already well documented, but that is not the only way through which software developers can leverage AI for customer service.
Using AI to deliver better customer service, software development businesses can keep current customers happy, attract more business and grow revenue and profits.
Software development has changed a great deal over the last few years, with AI contributing its quota to the changes.
As the use of AI in the industry continues to spread, more developers and testers will get on board with the use of AI systems to deliver high-quality results.
However, although Artificial Intelligence simplifies the software development process to an extent, developers must remember that it can also be used to give customers the best possible customer experience.
Overall, the future of AI-powered software development looks highly promising. Stakeholders can expect faster and cheaper software development cycles.
However, engineers need to be more prepared with the right skills needed to properly tap into the benefits of AI in their design and development processes.
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