Few technologies measure up to artificial intelligence (AI) in terms of the potential to drive innovation. It is transforming the banking sector, entertainment, e-commerce, healthcare, fitness, and many more industries, with scores of new fascinating advancements in these fields being on the horizon.
AI also excels in various aspects of software development. Moreover, this particular area might be the biggest beneficiary of the tech due to its self-learning proficiency combined with an unparalleled capability to quickly analyze and extract meaningful information from massive amounts of data. It is already increasing the productivity, speed, and quality of coders’ work.
There is no doubt that the further penetration of AI into the programming world will continue to redefine the way software comes into existence and what it means to be a developer. Let’s go over the groundbreaking changes in software engineering propelled by the tandem of humans and machines.
Developers Can Focus More on Creative, Complex Tasks
Although AI can hardly finalize a fully-fledged development cycle on its own at this point, it has already become indispensable for performing repetitive, tedious workflows that take a lot of time and effort when done manually. AI-based coding assistants such as Tabnine and OpenAI Codex are gaining momentum in this area. Their input into the programming process mostly boils down to automatic code completion based on thousands of open-source projects available on GitHub and similar resources.
Not only does this contribution save software engineers hours of typing and improve the accuracy of the coding routine, but it also frees up headspace to concentrate on design refinement, user experience improvements, problem-solving, and the creative facet of their projects.
The increasingly close “partnership” between humans and artificial intelligence will call forth a paradigm shift in which their roles will complement each other without overlapping directly. The next major milestone is that devs will have more of a supervisory function. AI, in turn, will do the bulk of the tedious work so that people’s contribution is restricted to putting the finishing touches on the process.
Mentoring for new developers is another realm where machines will maintain and increase their presence in the future. AI-based tools from this category such as Mendix Assist make the best coding practices accessible to IT enthusiasts who lack the expertise to complete projects on their own. This will pave the way for the democratization of this industry.
AI Makes It Easier to Spot Software Flaws Early
It is common knowledge that quality assurance testing is a lengthy and resource-intensive process with a bunch of pitfalls. Since software vendors can allocate a limited number of man-hours to look for imperfections in their products, some shoddy lines of code may fly under the radar and only be discovered after the solution has been deployed in a customer’s environment.
Artificial intelligence is already revolutionizing this territory. In fact, software testing is one of the most promising applications of this technology across the spectrum. It can traverse huge codebases to pinpoint syntax errors, logic inconsistencies, compilation blunders, and other bugs at a speed that’s beyond the capability of human analysts, and with super-high accuracy.
An interesting example of AI giving the quality assurance routine a boost is the use of image-based machine learning to test a program’s graphical user interface through side-by-side comparisons. Another technique called differential fuzzing introduces the same code to different software environments to observe runtime discrepancies, which helps identify subtle semantic and logic flaws. In addition to running tests based on predefined scenarios, AI-driven tools can learn from past projects and create new test cases. This cuts the development time significantly and ensures that the end-user gets a product of decent quality.
Real-Time Automated Error Management Will Keep Booming
When unresolved errors in software accumulate over time, this creates what’s called technical debt. That’s a common situation in ever-hasty development ecosystems that prioritize product delivery over quality. By neglecting to address such problems today, vendors run the risk of dealing with software downtime and glitches tomorrow. One more serious stumbling block is that the product may have security loopholes that make it susceptible to easy compromise. For organizations that provide software-as-a-service, these are particularly adverse scenarios because they impact their reputation and even a short period of inaccessibility entails financial loss.
Artificial intelligence and machine learning algorithms can fill the void. They automatically find errors, categorize them according to a preset severity scale, and fix them with little to no human involvement. This functionality is precious because the cost of addressing bugs increases substantially further down the software lifecycle.
The efficiency of an approach like that stems from the ability of these cutting-edge technologies to scour very large databases in mere minutes and unveil crude code or other deviations from the norm while suggesting steps that help software engineers correct them. High accuracy is on the plus side of this tactic, too. Moreover, AI can draw conclusions from previously analyzed data to fine-tune its error detection and management power. Given all the benefits, this trend will continue to gain traction in development circles.
AI Enriches Projects with Continuous User Feedback
To deliver a product that fully meets the requirements of its intended audience, IT professionals should put feedback front and center. This customer-centric approach provides actionable insights into what tweaks can enhance the user experience, which helps applications extend their reach dramatically.
Since machine learning algorithms can be taught to monitor various aspects of how users interact with a piece of software in real-time, they maintain a consistent feedback loop and save developers the hassle of collecting and organizing these tiny pieces of the puzzle. The incessant stream of user behavior data helps create a dynamic experience based on different usage scenarios. For instance, such software can flexibly adjust its UI manifestation on the go, including the size and position of its elements.
Requirements management is an adjacent process geared toward collecting, validating, and keeping a record of what end-users expect from an application. Unless done right, this activity can push back deadlines, inflate costs, or even disrupt projects completely. AI-powered solutions can take it up a notch. Trained on best-practice guidelines in this area of project management, they harness natural language processing to analyze requirements, identify inconsistencies, and suggest improvements.
These instruments can easily pinpoint escape clauses, incomplete or compound requirements, and various ambiguities, thereby minimizing the review time. IBM Watson and Visure Requirements ALM are examples of the best-known tools in this niche.
Planning and Cost Estimation Becomes Less Daunting
If you are familiar with the nuances of these preliminary phases of a software project, then you know what a burden they put on development teams. It’s like an equation with a bunch of variables that IT folks often fail to solve properly. Thankfully, artificial intelligence proved to be incredibly adept at estimating the timeline and the required budget.
Precise prediction is a matter of grasping the entire context of a project, including customers’ requirements, the hurdles that tend to arise when developing a specific type of software, and the amount of time it takes to tackle them. Machines can be trained to understand this metadata based on past projects and information from verified third-party sources.
The details about all these inner workings can be crucial to deciding whether or not to accept a project. Sometimes it’s more judicious to say “No” than take on a job that’s doomed to last inexcusably long. Being unable to hit deadlines is a catalyst for customer frustration, so it’s in your best interest to provide accurate estimates. With predictive analytics in its toolkit, AI can be the silver bullet in this regard and will continue to be a cornerstone for making informed business decisions.
AI Helps Step up Code Security
From early development stages to product release and deployment inside a customer’s infrastructure, security should be top of mind for developers. A good deal of data breaches and ransomware attacks happen because of coding slip-ups that spawn exploitable gaps in software. The only way to avoid these disasters is to find and fix such mistakes proactively. This hugely important strategy is known as SecDevOps.
AI is a programmer’s best ally in this regard. Machine learning algorithms can use information from publicly available sources such as MITRE’s CVE database to check every line of code for known vulnerabilities that turn an application into low-hanging fruit. The protection extends to all stages of the software lifecycle. After deployment, these intelligent tools can detect insider threats and zero-day attacks based on suspicious network activity.
The use of AI fits the context of the increasingly popular principle dubbed “shift left”, which aims to spot and repair imperfections as early in the development process as possible. This practice reduces costs and helps software vendors steer clear of the trap stemming from the above-mentioned technical debt.
On a side note, cybercriminals are already weaponizing this technology. Recent reports of riff-raff abusing ChatGPT to create malware exemplify the unnerving trend. With that said, further AI implementation to bake security into the CI/CD pipeline can make the pendulum swing in white hats’ favor.
Artificial intelligence is already integrated into the fabric of software development, and these ties will keep getting closer. Whereas some analysts argue that this technology is just about to make coders redundant, such predictions appear to be far-fetched. In the coming years, the evolution of this industry will most likely follow the route of deeper automation of repetitive tasks and processes in which the margin of human error is high. People will continue to write software, and AI will be lending them a friendly helping hand.