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Employing AI in the Agile Environment: Pros and Cons

By CIOReview | Monday, April 8, 2019

Every business function is today transformed by Artificial intelligence (AI); this also includes software development. To accelerate the existing software development cycle, Machine learning can be used; AI can redefine the ways developers build products. It is a challenge to introduce machine learning to augment software development process. Major application components like data management and software interfaces will make use of regular software, but ML techniques can be used into SLDC in many ways like coding assistants, automatic coding refactoring, making strategic decisions, providing precise estimates, analytics and error handling, rapid prototyping, using AI project planning, risk estimation, and project resource management. 

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On the basis of the project at hand, AI can provide training materials to developers to improve their skills and knowledge. Project delivery and onboarding can be done quickly. When AI allocates optimum workload to employees, 100percent of the staffs can be utilized. More time is available to make project-centric decisions if repetitive tasks are automated. In AI, the software engineer will curate data and input it into the learning algorithm. The model will be able to identify data pattern required to make decisions. If a test data is given to the ML algorithm, it compares the data with its existing database and makes decisions. The fascinating aspect about the whole function is that no knowledge of coding is required on the engineer’s side. AI identifies patterns that are complex for humans to recognize. It has transformed software development by uncovering human perception, program execution, and definition.

Agile can fasten the software development process as the developers can choose a smaller feature or group of features that are focused during two to four-week sprints.  Agile and Waterfall models are similar to each other at the primary level. In the ML software development model, developers must define the problem and list goals they would like to achieve, collect the data, prepare the data, produce the data into a learning algorithm, redistribute, assimilate, and manage the model. AI has proven to benefit businesses since its initial days, and many companies are leveraging the potential of AI to automate day-to-day tasks.

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