How artificial intelligence can improve software development process?

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How Artificial Intelligence can improve Software Development Process?

Today, Artificial intelligence dominates technology trends. It has impacted retail, finance, healthcare, and many industries around the world. In fact, by 2025, the global AI market is expected to reach an impressive $60 million.

AI has transformed the way the software industry functions. It brought precision, speed, and efficiency to the entire SDLC (Software Development Life Cycle). AI allows developers to focus on design and feature building rather than correcting errors in the code. This practice has vastly benefited companies providing custom software development services in delivering large-scale and error-free applications without delays.

This blog explores how AI facilitates the software development process and the top AI tools to optimize this intelligent technology-

#1 – Saves time with automated code generation

As writing code for a project takes a significant part of the development process, it is important to simplify it. In the past, the technique of writing code before development proved insufficient as it failed to recognize the target goals. Automated code can help resolve the issue in these circumstances. With AI tools that can complete code lines and suggest snippets of reusable code, developers save a substantial amount of time.

#2 – Runs quick and efficient tests

Efficient testing is crucial to ensure optimum product quality. The AI-powered tools find errors and fix bugs in the code, and ensure smooth performance in all the running environments. For example, a bug prediction tool at Facebook has an 80% success rate of finding errors and suggesting bug fixes.

AI also helps developers better formulate use cases and upcoming situations that can impact software performance. This formulation is achieved by collecting information from the system logs and providing automated test cases. These tools can also generate results of tests without performing the tests in reality.

#3 – Generates unique software designs

Clients are always looking for unique and out-of-the-box designs for their software projects. With AI development, digital assistants can conduct a thorough analysis of the projects’ requirements, highlight any inconsistencies, and make recommendations for improvements. More importantly, these tools work with natural language processing and utilize popularly referenced guidelines for training. The AI tools learn from past software projects and business factors to analyze the current projects’ performance and suggest improvements. This method immensely helps both clients and developers in designing better software.

#4 – Enables rapid prototyping

A prototype represents the development process and needs to be delivered in time for the clients to review the software. With machine learning, it can be possible to chart businesses’ functionality with a technical prototype that takes developers a lot of time. This way, developers can predict potential outcomes and accordingly modify the development process.

#5 – Automated project budgeting

Being overconfident about the project completion timeline is a common mistake that software companies make. Companies providing custom software development services can use AI tools to predict the project timelines better. They take into account the requirements, project scope. Factors that affected the past projects, its IT infrastructure, and so on, an automated budget and timeline can be generated.

There is a common notion among the developer community that there will come a time when AI and machine learning will replace software engineers. However, the truth is that these tools learn from developers and act as helping hands in building software in complex and large scale projects. Therefore, developers are always going to be at the forefront of building software.

Top AI and Machine Learning tools for software developers

There are four types of AI software –

  • AI platforms
  • Chatbots
  • Deep learning software
  • Machine learning software

It requires a thorough understanding of the client’s business requirements when it comes to custom software development. However, developers can’t possess the expertise and complete knowledge of every business. In such cases, Machine Learning and AI-powered tools can be used to provide a detailed analysis of every business needs. While developers can focus on planning and designing the software, they can gather all the required information.

Here is a quick overview of how AI software and tools can be used for software development-

Azure Machine Learning Studio is a web service that provides deployment of models on cloud and on-premise. It offers a drag and drop feature, scales easily, and does not require high-level programming skills.

Similarly, TensorFlow is an open-source software library for machine learning. Developing applications is possible with Python or C++ with this tool. It can be downloaded for free and also provides APIs for beginners to facilitate application development.

Cortana is a virtual assistant or personal productivity assistant, as Microsoft refers to it. It supports Windows, iOS, Android, and Xbox OS. Works on Bing search engine and can help users answer questions, set reminders, and so on. Moreover, Cortana is a multilingual platform.

Amazon Alexa and Google Assistant are widely popular virtual assistants. Alexa supports iOS, Fire OS, and Android, while Google Assistant additionally supports KaiOS. Both the platforms are multilingual. Alexa can be connected to the camera, electrical devices, and entertainment systems. On the other hand, Google Assistant supports two-way conversations.

Next is IBM Watson, which is a question answering system. It uses the Apache Hadoop framework to support SUSE Linux Enterprise Server 11 OS. With the use of natural language processing, it provides organizations with data analysis. It helps businesses make smarter and goal-oriented decisions.

H2O.ai is a deep learning platform. It provides insights into business functionalities and possible solutions to shortcomings and risk factors involved by analyzing comprehensive data. It uses R and Python languages to build models.

Here are some more notable AI tools – Google Cloud’s AutoML Engine that offers deploying models trained in external environments. Kite is an AI-powered coding assistant and with support from 13 languages and 16 editors. AIDA is a website designing platform that combines and analyses various software combinations and prepares a solution to suit its needs. Testim.io is an automated tool for testing software. It provides stable tests and super-fast authoring with machine learning processes.

Conclusion

Today, the software is one of the fastest-growing industries in the world. With an increasing demand for scalable, secure, and unique applications, there is tremendous pressure on the developing community. In such cases, adopting AI technology will provide simple solutions and provide a breeding ground for more innovation. AI-powered tools and Machine Learning are undoubtedly the future of software development, and adopting them is the best decision organization can make.

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