Artificial intelligence is transforming modern business and the modern way of life. Along with machine learning and continuous human support, artificial intelligence is seeing vast adoption rates across entire industries and sectors.
Software development and software testing are definitely two of the best examples of AI application as well as the use of machine learning and deep learning systems to achieve short, mid, and long-term goals.
Software developers and dev agencies can nowadays lean on AI and machine learning to streamline their processes and ensure better output and performance. But that’s just the tip of the iceberg.
Let’s take a closer look at some of the most interesting ways software developers can leverage AI and complementary technologies like machine learning to enhance software development and testing.
One of the most prominent use cases for AI and machine learning within the software development sphere is DevOps, and it’s interesting to see how DevOps is changing with greater AI implementation. Currently, AI is not rooted into every facet of the DevOps principle and practice, although it is one of its foundational pillars.
DevOps greatly relies on artificial intelligence to facilitate automation in development and testing, but also in operations management. This allows dev teams and operations to work seamlessly together, make incremental daily progress, and minimize the risk of human error. This is especially important in sectors that handle sensitive consumer information.
In the financial sector, for example, AI will play an instrumental role in shaping the future of financial services and software development through the DevOps mechanism. This is equally true for all other sectors where proprietary software development is on the rise.
It should go without saying that business leaders need to work with a software development company that utilizes the DevOps principle and leverages artificial intelligence to enhance the entire process. DevOps teams can leverage artificial intelligence to:
Artificial intelligence encompasses numerous complementary technologies and processes, including machine learning and deep learning. Deep learning is a part of machine learning based on artificial neural networks.
Unlike machine learning, however, the power of AI and deep learning lies in the ability to process unstructured data and thus eliminate or reduce the need for human oversight. Machine learning, on the other hand, requires human guidance and control.
You can probably already gauge the power and potential of deep learning to transform entire industries. In law enforcement, deep learning can analyze transactional data to identify fraudulent activity and patterns.
In customer service and support, companies can use deep learning to enhance their conversational AI solutions and bring smarter, more efficient chatbot software to life. The possibilities are virtually endless, and deep learning is currently one of the most transformative AI-based technologies in the world.
On a grander scale, AI and machine learning can leverage relevant data to create a digital transformation roadmap, but it’s interesting to see how it works on a granular level. The aforementioned conversational AI is a good example of how AI and machine learning can generate self-improving solutions with minimal to no human oversight.
For instance, deep learning can be supervised, semi-supervised, and unsupervised, allowing software developers to build better chatbot software and conversational AI solutions that improve on their own over time. One of the most interesting facts about AI is that it can analyze behavioral data and user trends, and collate vast amounts of conversational data to refine their messaging, improve their tone of voice, and most importantly, provide the right solution to the right customer.
Self-improvement is most evident in machine learning chatbots but of course, this is just one of many use cases that software developers can leverage to build more refined solutions. On the other hand, AI in the contact center can also be a great tool for empowering your human support staff with sentiment analysis and by analyzing key behavioral data.
Complemented by AI speech analytics, you can create an efficient monitoring process for your entire call center to enable continuous service improvement.
Another great example of AI application in modern software development is how AI enhances the web development process. Software and web developers can use AI at every stage of the web development lifecycle, but it’s important to note that AI really shines during testing and monitoring.
Artificial intelligence should be a key pillar of all modern usability testing software in order to facilitate the testing of websites as well as web-based applications, APIs, security, frontends, backends, and much more. Likewise, AI should be used for continuous monitoring.
Monitoring a website manually takes time and incurs financial waste, but AI-driven website monitoring and software testing is a good way to automate the process over the long term. This minimizes financial waste, yes, but it also improves results and outcomes because it eliminates the risk of human error.
As with DevOps, manual human testing is too unpredictable to produce stellar results on a continuous basis, so outsourcing to AI tools is a cost-effective investment.
Manually collecting accurate data to create a comprehensive project dataset is time-consuming and resource-intensive to say the least, and again, the right of human error is always present. This can be a futile task when developing resource intensive software like a B2B marketplace or a B2C Ecommerce store, or even Ecommerce apps.
You need to create clear datasets and eliminate data bias when planning and strategizing new software development projects to ensure a smooth development cycle from inception to roll-out.
AI-driven tools such as a dataset calculator can help you make accurate resource projections, such as the number of developers you’ll need, the complete financial break-down for the project, the timeframe for development, testing, fine-tuning, and rollout, and more.
AI and machine learning tools will analyze all the data you provide, even if you’re just guessing or are unclear on some of the project details, and give you a more accurate overview. You can then use your comprehensive data set to plan out a software development project efficiently and effectively.
During software development, it’s important to monitor the performance of your team in order to maintain the pace, eliminate setbacks, implement effective access control models and ensure complete software security until and after rollout. Developer productivity, however, is not just about meeting goals and deadlines on a weekly basis.
If you only look at what they have achieved, you will miss other crucial KPIs that could ruin your development process over the long term. That’s why it’s much more effective to gauge developer productivity and oversee innovative software teams using artificial intelligence, but also the SPACE framework.
SPACE stands for satisfaction, performance, activity, communication, and efficiency. These KPIs produce important data that you need to analyze to get a complete picture of the true state of your dev team.
Complemented by an AI tool that can analyze this data in an unbiased way, you can get actionable insights on how you can make your developers happier and more productive over the long term.
AI makes software development and testing a much more streamlined process that allows for faster development cycles and a quicker rollout time. Of course, there are many other ways AI enhances the development and testing experience, which is why dev teams need to start leveraging AI-driven tools to develop a competitive advantage.
In the oversaturated software development industry, speed, accuracy, and smart asset allocation allow developers to build winning software solutions. Most importantly, though, AI can help minimize the risk of human error while enhancing productivity and security across multiple development projects.
This allows small dev teams and large companies to roll out multiple solutions quickly.
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