How Artificial Intelligence can Improve the software development process?

As we know that Artificial Intelligence helps Industries in many ways and makes our industrial tasks easier and more efficient. This is only because we adopting AI technologies. Nowadays many Engineers want to start a career in these technologies and that is the main reason Artificial Intelligence and machine learning becoming trending technologies these days. Only because of these demands, it becoming the biggest boom in the market. By 2021, AI-enabled tools alone are expected to produce $2.9 trillion in business value. 80% of companies spend smartly on AI. 

 Artificial Intelligence has completely transformed the way of software industry operations. As per software development experts, the software development life cycle (SDLC) has tremendous benefits with Artificial Intelligence. It bought accuracy, speed, and efficiency to the entire SDLC (Software Development Life Cycle).  Instead of correcting errors in the code, AI enables developers to concentrate on design and feature construction.  In delivering large-scale and error-free applications without delay, this approach has greatly benefited companies providing custom software development services. 

Traditionally, creating a computer program requires you to specify exactly what you want the system to do in advance and then manually engineer all the features of your technology. It is possible to encode many tasks in an explicit way since computers were still quite powerful before the advent of AI. 

 Here we discuss how artificial intelligence can improve the software development process:

Automated Code Generation

Taking a business idea and writing the code for an enormous project is time-consuming and labor-intensive. Developers are approaching to opt for a solution that helps write code before beginning production in order to save time and dollars. With the complexity of what the aim of the target objective is to collect this information that, if you write the code from scratch, can be very time-consuming. 

By automating code generation and detecting bugs from the code, AI-based assistance reduces these loads to some extent. Take an example of a project where it can be interpreted by translating the concept into executable code in your natural language and framework.

Generate Unique Software Design

Designers need to apply their advanced learning and expertise to create alternative ideas in software engineering, preparing a project and developing it from scratch, before deciding on a definitive solution.

A designer begins with a solution vision, and then retracts and forwards the investigation of plan changes until the desired solution is reached. A tedious and mistake-prone action for designers is to decide on the correct plan decisions for each step.

A few AI advances have shown the benefits of developing conventional approaches with intelligent specialists along this line. The catch here is that the operator conducts the customer as an individual partner. This partner should have the opportunity to provide sufficient input on the most professional method of designing projects.

Take the AI Design Assistant (AIDA) example, most designers are able to understand the client's needs and preferences as well as use the same to design a similar project.  AIDA serves as a website building platform that helps to explore various software design combinations by providing the required personalized design according to customer requirements.

Software Testing

The central part of every software development cycle is testing. A big problem for development teams in the identification and avoidance of errors or bugs. Fixing bugs and errors consists of a large amount of software development costs. Throughout the development life cycle, early error detection requires continuous monitoring. Current software testing activities, however, are expensive, costly, and time-consuming since errors are discovered in the code in several instances after the product has been developed and distributed to the mass market.

In less time than manual testing, AI and Machine Learning algorithms will ensure that the test conducted is error-free, allowing code testers to concentrate on more critical tasks such as maintaining code. Prototypes of AI-enabled code testing allow development teams to conduct mass tests on thousands or millions of codes. Development teams can deal with case-specific experiments, whereas routine and time-consuming tests can be done by AI-assisted automation instruments. Ultimately, this results in a decrease in errors because AI-assisted tests scope and correct errors with sheer accuracy, leading to an increase in overall software quality improvement.

For instance, with AI-enabled cloud testing, DeepCode assists developers to quickly test and release working code. Software testers use a parameter template test plan and add the code to an AI tool, which transforms the code into a working test case automatically and fixes specific bugs. Engineers are sent requests for approval for bug fixes immediately, and subsequent deployment to production. Not only does this save time, money, and energy, but the organization also generates a high ROI.

GUI Testing

In communicating with today's applications, Graphical User Interfaces (GUI) have become important. They are increasingly used in sensitive systems and it is important to test them in order to avoid failures. Testing GUIs is difficult with very few tools and techniques available to help in the testing process.

Currently, GUI testing methods are ad hoc. 

The methods of GUI testing currently used are ad hoc. They require the test designer to perform humongous tasks, such as manually designing test cases, identifying the conditions to be verified during the test execution, determining when to check these conditions, and finally assessing whether the GUI software is properly tested. Phew! Phew! That's a great deal of work now. 

Applitools is an AI-empowered GUI tester tool. The Applitools Eyes SDK automatically checks whether or not the visual code is running correctly. Applitools allows users to test their visual code as extensively as their usable UI code to guarantee that the application's visual look is as you intend it to be. To ensure that they all suit the template, users can test how their application looks in different screen formats.

Deployment Control

Machine learning and AI technologies have also had some effects on the deployment of software, such as an improvement in the efficacy of deployment control activities. The deployment process in the software development paradigm is the stage where developers often update programs or applications to newer versions. By analyzing details such as statistics from prior code releases and application logs, AI-powered tools help predict deployment failure ahead of time. In the case of a failure, this can speed up root cause analysis and recovery. In one instance, automated deployment and rollback verification based on machine learning helped an e-commerce company achieve faster application delivery and a 75 percent decrease in mean-time-to-restore from a development environment failure. In manufacturing, AI can also help applications run optimally. A machine learning-based tool has been deployed by another online company that analyzes various possible application runtime settings and automatically deploys optimal environment settings. This allowed them to halve the cost of clouds and more than double the efficiency of applications.

Requirement Gathering

As a conceptual process of SDLC, the gathering of requirements involves full human interaction. A wide variety of techniques/tools such as Google ML Kit and Infosys Nia are provided by artificial intelligence to automate certain procedures to eliminate human interference to some degree. This stage requires a lot of attention on the early identification of loopholes before moving to design. An AI method called Natural Language Processing can allow machines to understand the specifications of the user in natural language and extract high-level software models automatically. There are, of course, some problems with this approach, including difficulties in balancing the systems created.

Conclusion

Artificial intelligence tends to have a huge influence on software design and development. Software development companies are expected to take on the possible advantages of AI, which is a game-changer in the development of software.

Also Read: Uses of Artificial Intelligence and Machine Learning in Airline Industry

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