The IT industry is currently experiencing the next transformative wave, fueled by the emergence of multiple LLMs and generative AI. Exploring generative AI-based use cases in application development, maintenance, modernization, and testing presents multiple advantages, significantly boosting efficiency, accuracy, and automation in these domains.
In the realm of application development, generative AI can streamline the requirement gathering process, assisting in story refinement, elaboration of definitions of done, and recommendations on non-functional requirements. Furthermore, it can accelerate coding tasks by automatically generating code snippets or complete modules based on high-level requirement specifications, story documentation, or flow diagrams.
Generative AI can play a crucial role in maintenance and support by automatically identifying and fixing issues or vulnerabilities in code. By harnessing machine learning algorithms and generative AI capabilities to analyze historical data patterns, operations teams can be better equipped to proactively prevent issues and incidents, thus minimizing downtime, and ensuring smooth application operation. Additionally, generative AI can assist in documentation generation, facilitating better code maintainability and enabling faster root cause analysis through log inferencing and summarization, leading to improved MTTR. Assisted ChatOps is another key use case currently being piloted and implemented.
The modernization and migration of workloads to the cloud represent critical aspects in the application services space. Generative AI offers numerous possibilities and use cases for assisted reverse engineering, code summarization, configuration analysis, dependency analysis, and integration complexity analysis, thereby enhancing velocity and outcomes for modernization initiatives.
Generative AI can enable the creation of synthetic datasets and test cases that mimic real-world scenarios, ensuring thorough testing and uncovering potential issues that might remain undetected with traditional testing approaches. Automation of test case generation and execution through generative AI not only would accelerate the testing phase but also enhance test coverage, leading to more reliable and resilient applications.
Our team has recognized the significant potential of generative AI, particularly in application development, maintenance, modernization, and quality engineering. While exploring numerous use cases, we have identified a key set of low risk, high value ready-to-implement use cases, culminating in the development of TechM AppGinieZ.
Unleashing the Power of Generative AI with TechM AppGinieZ
TechM AppGinieZ is a comprehensive UI-based intuitive solution that delivers multiple text-to-text, text-to-code, image to text, and image to code use-cases. Leveraging the power of LLMs coupled with advanced prompt engineering, it enriches LLM outputs through documents, data, and content, bringing context from the enterprise knowledge base. Additionally, it features reinforced learning through human feedback.
The solution covers a wide range of functionalities, including requirements refinement, assisted test case generation, test script generation, code reverse engineering and documentation, log and document inference and summarization, assisted incident and defect triaging, assisted code generation, tools recommendation, and assisted ChatOps.
Furthermore, TechM AppGinieZ seamlessly integrates within Tech Mahindra's New Age Delivery (NAD) platform, providing an integrated DevOps workbench and IT value stream management solution for engineering teams. This integration enables the utilization of AppGinieZ capabilities across the application services lifecycle, from inception to production. Moreover, AppGinieZ is an adaptive solution that seamlessly integrates with existing DevOps tools in customers' landscapes.
Ensuring Security for Trustworthy Applications
Security considerations are paramount when leveraging generative AI capabilities. TechM AppGinieZ addresses this need by integrating strict security guidelines, periodic vulnerability assessments, and data masking technology, ensuring security and privacy throughout the entire process.
Empowering DevOps Teams
The integration of TechM AppGinieZ into the TechM ADMSNXT.NOW solution stack signifies a significant advancement for our DevOps teams. This inclusion enables teams to harness the revolutionary capabilities of generative AI, resulting in a considerable enhancement in overall efficiency across the application development lifecycle, including requirements, coding, maintenance, and testing phases. Moreover, it minimizes human errors and empowers engineering teams by automating certain aspects of the development and testing processes, enabling them to focus on more creative and complex problem-solving tasks, thereby resulting in higher-quality applications.
A New Era of Application Development
TechM AppGinieZ takes us into a future where our application development, maintenance, and modernization services reach new heights. By unleashing generative AI's potential, we are empowering our engineering teams to redefine productivity and efficiency. This solution truly enables a paradigm shift towards faster, more secure, and unparalleled service delivery.
As we embrace this transformative era in application services and embark on this journey of transformation, we invite you to reach out to our team to learn more about the work we are doing in this area and discover more about TechM AppGinieZ at ADMSNXT.NOW@Techmahindra.com.
About the Author:
Global Practice Head – ADMSNXT COE, Tech Mahindra
Anjali Chhabra is a technology leader with over 24+ years of experience in IT engineering services industry, with specialization in optimizing software development lifecycle (SDLC), DevSecOps transformation, Application Modernization and Quality Engineering services. She has worked with large enterprise customers ranging from banking and financial services, telecommunication, insurance, RCG, technology, and various other industry verticals. She has a proven track record on DevSecOps transformation initiatives, Application modernization (on-prem and cloud adoption) consulting, automated delivery pipeline framework design and set-up for large enterprises, community of practice (COP) set-up for various niche areas like Microservices & Containerization, Observability & SRE, Digital Integrations, Cloud Native Engineering, Quality Engineering. In her role, she has also worked with diverse IT teams globally on consulting and implementation of SDLC optimization with usage of Hyper-Automation predictive analytics, machine learning, and AI.