Software. It's the ocean that we all swim in, day in and day out. From the apps we rely on from dawn to dusk in our personal lives to the major applications and systems we use to accomplish myriad strategic business objectives, software truly makes our world go around. So, when there's a software glitch or breakdown, it can totally snaggle the day and hamper your agility and ability to progress in those critical objectives. Nobody has time for glitches. Yet, over the past few years, artificial intelligence has become an incredible tool in smoothing out the inevitable lumps that come from solely human-developed software. It can seem like a mind-bending concept: AI was developed as a software concept by humans, and now AI is perfecting the software development and testing process in entirely new and exciting ways, which is freeing the human minds behind software creation to reach even higher levels of innovation.
As of today, in fact, developers are using AI to generate new code, review and revamp freshly-written code, test software more thoroughly than ever, catch bugs and optimize entire software development projects with impressive efficiency and accuracy. AI-assisted software development streamlines previously arduous development timelines, boosting efficiency, saving time, saving money, and even helping a new generation of software developers learn how to code better from the beginning.
Trends in Artificial Intelligence-Assisted Software Development
Using artificial intelligence in software development isn't the trend; it's how AI is being used to optimize and develop new applications and streamline entire development projects, that's is the real story here. The Wall Street Journal reports that businesses expect AI tools to pump out a massive $2.9 trillion for businesses as we enter 2021. They further report that AI, while it could eliminate up to 1.8 million jobs, it should also create 2.3 million new jobs that run the gamut between highly-skilled to even entry-level developers. And Forbes reports that they expect the global demand for custom AI-developed software to increase to $61 billion by 2023. The strong demand for AI is being driven by a host of potential advantages.
Three Advantages of AI in Software Development
Using AI in software development makes the entire software design and development process faster, cheaper and less buggy. And contrary to worries some might have, putting AI to work on software creation and testing does not mean that jobs held by human software developers are being farmed out to robots. In fact, Deloitte reports that the increase in demand for great software apps means that demand for software developers is expected to expand at least 21 percent by 2028 So where does AI fit into the software development picture with human developers? AI development tools are making the jobs of coders, testers, business analysts, and project managers more productive and efficient. AI is empowering them to churn out higher-quality apps in shorter time frames, for less expense. It also boosts the competitive advantage and agility companies have when they can create custom software using these tools. To dig in further, there are three very specific advantages that AI brings to software development and software developers themselves.
1. Significantly Improving Precision
Even the most highly-skilled software developers and testers can slip up occasionally. Even the most brilliant ones are still human. They get tired. They get distracted. It's normal. That means that mistakes and glitches can creep into the code on the development side, while testers may miss bugs during the QA process. New AI tools on the development side can capture even tiny details and mistakes from one angle, while standardizing testing steps that can be implemented precisely and exactly during each testing phase. This drastically reduces the number of bugs in any software system and helps create specific, refined, reliable, high-quality outcomes.
2. Incredible Support for Developers and Testers
Software programmers often have to create a ton of code, which can take a lot of time and expense that might be better spent on new projects and developments. So, for programmers, AI code generation tools simplify the more mundane and time-consuming code generation tasks. This can cut down on errors and give wings to creativity. In fact, developers are now creating new code that allows for even more automation based on machine learning. Such tools can also squash more bugs even before a project enters the testing phase. Artificial intelligence testing tools are also helping testers fine-tune their testing regimens. For example, it helps them verify code and databases and create test plans that increase their ability to catch even the tiniest bugs. Let's look at more specifics:
Advantages for Developers
AI tools help developers and project managers throughout the entire software development lifecycle, including at the initial requirement gathering and analysis phase. The very human process of ideation and conceptualization at this point can get a boost from AI that can discover issues before the real work gets underway. This reduces future problems and saves considerable time and headaches down the road. It also gives more light to new possibilities for delighting customers with masterful, smooth user experiences, and results.
Artificial intelligence can cut down on human errors at design time so that developers can devote more energy to creating a really customer-focused design that fits the client's needs best. Some AI tools can then help generate some of the standard code, error-free, which speeds up the development process and gives developers more opportunities to refine critical details for the end-user. AI can also verify and secure code, looking for anomalies that can signal data breaches and keep the product and clients safe.
Advantages for Testing
Artificial intelligence provides software testers with tools to leverage the total depth and breadth of tests, which can massively improve overall software quality by eliminating even hard-to-detect bugs. This is especially true of programs that have a lot of data to test. The bigger the data volume, the harder it is to guarantee that testers won't make errors themselves as they examine it all. But AI tools can correct databases automatically to eliminate the problems. Testers can then devote more focused energy to probe the storage capacity, data tables, document content, states, and more to determine whether or not the software performs up to the highest standards of excellence. Given the fact that developers can catch more bugs even before software makes it to QA, artificial intelligence tools can help companies turn out rich, smooth, agile, and high-quality software faster and more reliably than ever.
3. Boosting Competitive Efficiencies from Project Management to Deployment
Artificial intelligence tools are a huge boon to project managers in charge of complex software development schemes. The benefits start even at the beginning. Once the very human brainstorming work is underway, and a new application is conceptualized, the project team can use AI to kickstart the requirement gathering phase, along with analysis of required resources, workflow predictions and timelines. AI can help weed out problems before programming even starts, assisting teams in predicting with more accuracy how much time a project will take to get to the deployment phase. By employing advanced analytics tools, machine learning helps project managers drill down to the needed data to evaluate potential risks and technical issues. It can help accurately predict resource needs with more precision, cutting down on unexpected expenses and delays. With more accurate measurements and predictions for workflows and timelines, project managers can deliver the product on time, on budget, and with higher customer satisfaction levels because of smooth, high-quality software delivery at deployment time.
Spark Equation Engineers’ Experiences Using AI for Software Development
As previously mentioned, the potential for AI to revolutionize processes, workflows, and outcomes for everyone who touches the software development process is considerable. In practice, the jury is still in deliberation. For now, the majority of resources possess information about testing AI's abilities to generate text. The latest update is GPT3 autoregressive language. Many sources report that people simply can't tell the difference between normal text and GPT3 generated text at all. That is already an impressive accomplishment. The next step is to apply and integrate this type of model into a toolset that can generate code. Of course, the logic of text is quite different from the logic of programming. If we count the differences in various programming languages, then developing the AI toolset to manage this becomes somewhat challenging. At this time, it is too early to tell how well it will work in reality.
Nevertheless, testing and experimenting are already well underway. Our software engineers are not only waiting and watching how this will play out, but we are also working with our own AI models to devise what new ways technology can push us forward to the future. Here at Spark, we are already working with AI to develop agile, high-impact systems to empower our clients to pursue success in their endeavors.
Considering How to Use AI in Software Development?
Folding artificial intelligence into the project management, software development, and testing environments has the huge potential of building a new paradigm that makes businesses even more agile and up to managing unpredictable, yet inevitable changes. AI has the potential to help developers quickly develop new, clean, creative code that can be swiftly evaluated and modified for an ultra-high-quality user experience. Software testing with artificial intelligence and machine learning has the potential to quickly and efficiently catch all the bugs and fix them before deployment.
This should result in bringing excellent solutions to market faster, saving time, money, and headaches. Instead of getting testers bogged down in endless cycles of bug detection, they can focus on user-experience issues on a more granular level, so that development teams can churn out a more satisfying software application. By streamlining the number of options and issues developers and testers need to worry about, they are freer to develop and create with best practices in mind. Our Spark developers believe that if we can embed an artificial tech lead inside the product that can automate, guide and validate the developers' work, we can deliver the best software solutions, at scale, to attain your strategic objectives.
What are some of the ways you're considering using AI in your applications? What concerns and questions do you have about how you can get the most out of artificial intelligence in your organization? Let us know in the comments below, and if you have specific questions, let's talk about some of the ways we can help you get the most out of AI software development now, and in the future.