AI, ML are the hottest topics in the IT market today. There is a statistics that says just like how Computers were booming in late 80’s and early 90’s, between 2018 and 2022, AI and ML are the key subjects in the Software industry. AI is becoming more user-friendly and knowingly or unknowingly we are using it in our daily routine works. Be it Alexa, Google Home, Siri, iPhone X etc.
It certainly revolutionizes the commercial world. Perhaps I took a step back and was thinking slightly from a different dimension about AI and ML usage. How it is going to be more useful for a Software Engineer. Being an Agile fanatic and Automation Tester for 8 years, I started doing this research thinking about one question in mind – “How AI is going to help Agile ?”
Many organizations work on tailored Agile fashion which is absolutely great for their purpose until BAU works fine for them. Some organizations have separate testers in their Agile team(s) and some don’t, making everyone in the team to do everything. I started this research thinking from organizations that have separate Testers within their Agile team whose primary responsibility is to perform functional testing and automated acceptance tests.
Considering the reality, as the first step of my research, I started interacting with SMs and Testers identifying key issues. Agile development is one of the best ways to deliver a product with a limited number of user stories to be developed and tested. There are some situations in which development of certain stories takes so much time during the sprint because of certain unexpected unavailability of dependencies. This eventually leads us to the question – “Are we delivering a quality code/product?” during the retrospectives. Testers in these teams have very less time to perform testing. Automated Acceptance Scripts can help up to very less percentage here in this situation as there is again an element of dependency on availability.
This is where I realized the usage of Bots. Building Bots having the capability of performing several predictive analysis can massively help the Product Owners and Scrum Masters to decide the backlogs, predict the dependencies availability and of course to perform regular deployments and testing. These Bots by nature can have self-learning methods to adapt to the nature of the Agile team and suggests the predictability on the scopes of the user stories to be developed.
Nevertheless, being an Automation Tester I prefer a guidance Bot which will practically help me with bringing the concept of Automated Automation live, by giving me auto-generated tests scripts reading the Stories directly from the tools like JIRA. A Bot with Orthogonal Array knowledge helping the testers to perform testing from 360 degrees angle.
These are just the beginning thoughts of my research and will publish more on these cases to the Agile fanatics out there to make their delivery even faster and more assured.
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