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Art of Intelligent Automation



Reshaping the future of work with robots


As Business Applications become complex, so the testing going to be. With traditional test practices its next to impossible to maintain quality of business applications in long run.


Keeping same in mind, now Companies like to introduce automation as soon as development gets started.


This approach not only help in maintaining better quality of business applications but also result in significant cost savings in terms of time and effort.


And there is no better solution other than Intelligent Automation.


This article we will share some be best practices which Next Generation Automation team proposed to the clients and done some successful Proof of Concept in recent past.


Practice 1: Structured Data Interaction (SDI)


These are traditional systems where the integration is through exchange of information that is well structured. Examples include integration of systems through relational data base management systems ( RDBMS) , data transformation tools, and application programming

interfaces ( APIs) and web services.


More intelligently we streamline structured data interaction using automation among both homogeneous and heterogeneous systems, better the flow of information and resulting better product quality.


Practice 2: Robotic Process Automation



It involves automation of standardized and rules driven system-based activities using scripts and other methods to support efficient business processes. It is suitable in scenarios where it is too expensive or inefficient for humans to execute a task or a process. Very helpful for tasks that involve repetition with out change.


Use Case for Testing can be All failures tests automatically converted into test defects and submitted to Defect tracking tools like Jira for defect analysis and defect fix.


Practice 3: Machine Learning


It involves systems that learn through handling variations that are not anticipated upfront. These systems get trained on the go by assimilating leanings from the data and decisions, and may make simple predictions or classifications backed by algorithms. A simple case could be a scenario where a well-defined identifier needs to be mapped to more descriptive/free form text, e.g., mapping of a vendor name that appears on an invoice to the vendor ID in the system. The vendor name may appear in various forms.


Machine Learning results significance reduction in test effort if implemented rightfully in your application test cycles.