Aspire’s Aju Mathew on AI and Advanced Application Development

hi I'm James Maguire and on today's video we're talking about the many ways that generative AI can help with software development this ranges from backend and front-end development to migration and maintenance issues and also includes testing of software to discuss that I'm joined by someone who knows a lot about the topic with me is Azu Matthew vice president software engineering at Aspire systems as you very good to have you with us today thank you for having me here James and uh nice to be speaking to those audience so I know that generative AI plays a large role in software development specifically in terms of developing in a green field situation how can professionals use gen AI in backend and front-end development yeah let me start with backend development um um the most simplest use case uh for backend development is for the developers to use um gen platforms to kind of generate small Snippets the business Logic for the apis that they're developing that's on the simple use case side on a more advanced use case is for users to or the developers to use the platforms gen platforms to generate all the backend apis as per the um you know if they have a lowlevel design spec then as for that document this would kind of generate all the uh crud operations and lookup operations for all the business entities that we would have defined in the low level design and um this would typically require Advanced prompt chaining and appropriate parameterization and from a parameters perspective you can look at changing the specified language in which the code gets generated with Associated best practices it would also follow you know specified architectural and design principles uh what you would specify as parameters you can also look at automating the unit test cases for the backend apis um um which get generated using a you know the specified language framework for example if it's junit for Java and nunit for net and we can also generate uh API or Swagger documentation for that so that's on the backend side of development similarly if you look at front end development on the other hand an advanced use case is for users to generate uh frontend web pages in in Technologies like react uh angular or or View for that matter right based on the UI screens that are being built uh on um the interface design software like figma by uxix designers so you can actually generate um frontend web pages from that actually directly you can also generate web pages with a micro frontend architecture as well so all of these can be done including unit test uh cases for the fronted logic um can be generated using the respective Frameworks for example uh for angular you could probably uh generate scripts which are applicable for Jasmin and stuff like that interesting well on a similar note what about in a Brownfield situation what's important to know about gen AI usage in migration and maintenance yeah this is a slightly more um slightly more complex use case Brownfield because now you're dealing with you know a situation where you have an existing codebase right so but here itself you you'll have multiple scenarios um like we talked about right uh we have from a maintenance standpoint um users can or the developers can use um generative AI platforms to uh basically generate Buck fixes or fixes for the code issues or basically have incremental business Logics for um any API functional changes basically tools like um Amazon Q support functionality already and we have seen that um you know available in the market as as we speak and um users can also use U um these platforms to reverse engineer code so basically extracting the business logic and documenting it and this is basically useful for understanding Legacy code bases um so that's on the maintenance standpoint similarly from a migration standpoint users can and this is you know again an a Advanced use case users can use the generative AI platforms to upgrade the technology Frameworks for example there are gen platforms like again I spoke about Amazon Q earlier it supports uh you know Java upgrade from an older version to a newer version so these are some of the use cases uh you know that I can think of for both migration and maintenance in a brown field scenario CH well what about testing because it's such an important area it what are the essential points of using gen Ai and testing specifically functional testing and performance testing yeah in um in functional testing um users or you know the test test automation users can use these platforms to generate automation scripts for functional testing of the front end uh screens um as well um in probably languages like csharp or Java for automation tools like selenium with all the best practices Incorporated similarly you can also generate automation test CPS for functional testing of the backend apis as well and the input you know for the J platform would be the Swagger API documentation uh that we would have generated as part of API development which we spoke about in the earlier question so that's how it helps in functional testing similarly for performance testing you know the performance testers can use the Genera AI platform to generate per performance test scripts uh for performance testing in tools like J meter and and the likes so that's how uh it would work uh James do you think these these uses you've been talking about for testing or maintenance uh are these common place now or is this still coming on the way emerging um uh it's still emerging uh especially on the test test automation side it's still emerging but uh the one that I talked about in Brownfield and all of that it's already there to to a large extent but on on the testing side it's still emerging interesting all right well I always think it's interesting to to look ahead to look to the Future i' like to get your thoughts what do you see going forward as the future of geni and software development and testing I'm sure there's a lot of interest in that question this is uh this is an interesting question definitely James and going forward I would um anticipate um developers being able to do much more with the Gen platforms and software development like probably generate complex business logic because we talked about simple business logic or just pure crad operations but going forward uh having options to generate complex business logic along with the API generation that is one and uh complex use cases of cloud application migration modernization and all of that whether it's on Prem or on the cloud all of those things being able to be done using a a gen platform now that on the development side similarly for testing I would kind kind of anticipate um you know the test automation folks to be able to generate test scenarios and test cases from the business requirements themselves using such platforms it is futuristic but it is it is possible and and that's what that's where I see it going in the next couple of years it's fascinating it's really going to be a really interesting field to watch uh as you thank you so much for sharing expertise today I learned a lot very much appreciate it thank you you're welcome James

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