Digital Transformation And ITSM
Technology Adding More Dimensions To Business
Welcoming New Changes with Technology
Visualizing Big Data to get the Big Picture
Creating Integrated Workflows for an Evolving Company
Mike Everly, CIO, D&H Distributing
Key Technologies Driving The Stampede to the Cloud
Mark Bagley, VP, Innovation Scouting, BT
Cloud Considerations in Asset Management Intensive Organizations
Hart Levy, Director, Management Consulting - EAM & MRO Spares Inventory, Hitachi Consulting
Adapting Asset Management to a Cloud-Centric Reality
Leo Barella, VP & Chief Enterprise Architect, AstraZeneca
Making An Efficient Software Development Process Using Artificial Intelligence
Fremont CA: Technology tools are set to change the traditional processes of software development. Artificial intelligence (AI) and machine learning (ML) technologies are about to revolutionize the human-driven era of writing Rule-based codes which solved deterministic problems using logic. These tools allow enterprises to select the right data to train the neural network to solve a problem without any human intervention.
Even though the AI technique for software development is in its infancy, still AI tools can augment conventional software development. Here is an analysis of some ways in which enterprises can use AI and ML tools to manage software development:
Creating an MVP fast: Machine learning techniques can shorten the Planning and preparation level of a project to a few lines of codes, which was a daunting task for traditional programming processes. These tools provide essential resources in getting the project to the prototype level. For example, AI and ML tools can create chatbots using natural language libraries, or a user-friendly no code platform.
Managing the project: AI tools can analyze past data to provide accurate estimates about the project delivery time, possible delays, and other details. Deep learning techniques can detect patterns with a detailed log of past projects including bugs, estimates and actual values with user stories and reviews. This allows enterprises to remain within the time and money budget, and stay true to the initial contract.
Automatic debugging: The deep learning algorithm can help in speeding up the debugging process by flagging known errors. AI tools could also be used to correct a wide range of errors with adequate training and data.
Automated Testing: Testing a code plays a crucial part in quality assurance of a software product. AI tools can automatically create test cases by looking at the past logs. It can also predict the outcome of testing without performing actual tests.
Code generation: Pattern detection techniques could help AI systems to generate software code by putting together some predefined modules. It will allow companies to use their workforce in more productive ways.
Although AI systems are still in its nascent stage, it will grow in importance to provide ample support for the developers. The tools will be able to shorten the product development cycle with new and innovative techniques.