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Artificial Intelligence (AI) is transforming every industry possible, and software development is no exception. Software is at the base of all the advancements we see in our lives today. Software development technologies have come across a huge transformation over the past few years. AI accelerates the traditional software development technique and opens the door to easy programming.
Companies developing software are working on enabling rapid behavioral changes to develop and release products with speed and accuracy. Traditional software development is not designed to support these changes, involving a series of successive stages including manually writing code, preparing requirements, designing software, and testing to establish that the final product meets specifications. Artificial intelligence is invading this process by creating scalable and efficient workflows to drive productivity and reduce time-to-market. AI algorithms and advanced analytics allow software development teams to make instant decisions using real-time data at scale.
Artificial intelligence is making the process of designing, developing and deploying software faster, better and cheaper. According to a survey by ZDNet, around 61% of the IT managers report that AI is drastically changing software development and deployment process, and 57% expect it to similarly influence the software deployment process. Those who have already implemented AI are more likely to report a strong impact on both software deployment and development. Large and small vendors have launched dozens of AI-powered software development tools over the last two years. Start-ups offering AI-powered software development tools raised US$704 million over a twelve-month period ending in September 2019. AI is unravelling a huge number of possibilities without replacing programmers. Henceforth, Analytics Insight brings you a list of digital innovations that are transforming AI deployment in software development.
Coders spend a majority of their time reading documentation and debugging code. However, using intelligent programming assistant will minimize the job. Smart programming assistant reduces the time taken for developing code by offering real-time support and recommendations such as relevant document, best practices, and code examples. For example, Kite acts as an assistant for Python.
Machine learning is an easy subject for people coming from a technical background. Open-source machine learning (ML) frameworks are an innovative digital solution altering AI deployment in the software development pipeline. The framework can lift heavy workloads and facilitate projects from scratch. By leveraging open-source machine learning framework, coders can train their own image recognition system, music manipulation or language processing model.
Developers can't always be positive about clean codes. Such clean codes are critical for team collaboration and long-term maintenance. However, as technologies develop, it is a necessity to have long codes without error. Machine learning can be used to analyze code and automatically optimize it for interpretability and performance.
Python is one of the best Internet of Things (IoT) programming languages for developers. Ultimately, Python libraries allow coders to define, optimize and evaluate mathematical expressions with multi-dimensional arrays. With the help of Python library, coders can achieve competitive speeds that rival C-implementations for problems involving large amounts of data.
Bots that support software development are seen as a promising approach to deal with the ever-increasing complexity of modern software engineering and development. Bot services provide a resource to build, manage, test, and deploy sophisticated bots in one single location. With many bot services, coders can also code specific commands using C#, JavaScript, or Python programming templates for local development.
