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.
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.
Taking DevOps to the next level
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.
Leveraging deep learning for advanced solutions
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.
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.
Conversational AI can improve itself via machine learning
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.
AI in website testing and monitoring
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.
Using AI for dataset project analysis
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.
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.
Gauging developer productivity with AI-driven tools
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.
Over to you
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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