ISG Provider Lens™ Next-Gen ADM Services - Application Quality Assurance - U.S. 2023

DevOps and SRE lead the way for application development and maintenance with AI support

The application development and maintenance (ADM) domain has undergone multiple changes over the last few years. These changes have been fast paced compared to the previous generations of application development. With organizations undergoing digital transformation, speed, cost and agility play a vital role in ADM. Enterprises seek partners that can help them realize the potential of digital innovation. Service providers have embedded this aspect in their digital offerings that are aligned with the most advanced ADM methodologies. The graphic below showcases the periodic changes in the ADM space.

With the advent of new approaches and methodologies, there is a significant shift in the adoption of Agile and DevOps methodologies for application development. Increased emphasis is placed on developing applications aligned to enterprises’ digital journey using Agile and DevOps methods. This engagement enables faster time-to-market, enhanced collaboration and improved quality. It also enables an innovative approach to delivering applications aligned with specific business requirements. With businesses’ economic constraints, cost optimization has become a key focus area. Application maintenance has witnessed the use of technologies, such as automation, analytics and AI, to optimize the entire app maintenance process and reduce human intervention, delivering cost savings that can fuel new initiatives in app development for enterprises. Site reliability engineering (SRE) adoption has contributed to enhanced reliability, predictable operations,  performance measurement and qualitative applications development. DevOps and SRE act as balancing factors to deliver a high-quality application. DevOps enables the disintegration of traditional silos into development and operations to improve the efficiency and reliability of software development and deployment processes. SRE focuses on creating highly scalable and reliable software systems while increasingly emphasizing automation and monitoring.

Hence, DevOps and SRE are crucial in optimizing software development, deployment and operations processes. They contribute to the overall efficiency, reliability and success of digital products and services.

The increased adoption of AI and generative AI has a significant impact on the ADM lifecycle. Some of the use cases include the following tools: AI-driven predictive analytics that aids project planning by predicting resource requirements and potential bottlenecks; AI-driven design tools that generate user interfaces, layouts and prototypes based on user requirements and design principles; AI-powered testing tools that automate test case generation, increase test coverage and help quickly detect defects; AIOps platforms that use AI to monitor and manage application performance, predict and prevent downtime and optimize resource usage; predictive maintenance powered by AI that analyzes historical data to anticipate maintenance needs and proactively schedule updates; and AI-driven analytics that provides insights into application performance, user interactions and usage patterns, contributing to continuous improvements. Generative AI can assist developers by generating code snippets based on natural language descriptions or completing code lines. While AI has significantly impacted the ADM lifecycle, the generative AI impact is not entirely proven across the use cases. With these changes in the industry, the workforce plans to combine humans and machines. A combination of bots that perform routine tasks and cognitive functions and encompass analytical abilities will assist the human workforce.

In addition to these technical developments, there is an increased focus on establishing global capability centers (GCCs) that support businesses in the U.S. These GCCs are typically established in India, eastern Europe or Latin America. GCCs established in India had a tremendous increase in delivering innovative and cutting-edge applications and products developed in collaboration with service provider partners.

Top trends in ADM segments are listed below.

Agile application development outsourcing: The use of AI across application development and business-led, cloud-based application development are two major trends observed in this segment. Enterprises seek application development with an AI component in their contracts. They focus on transforming monolithic applications by incorporating them into cloud architecture. This process requires significant investments in upskilling the existing talent pool. Most application development is driven by enterprises’ digital agendas, with an increased emphasis on delivering application development through Agile, DevOps and SRE aligned with digital product-oriented development (POD) models. Some of the unique contract models being used include experience-level agreements mapped to business imperatives.

Agile application development projects: Enterprises focus on CX and plan to emphasize the delivery of exceptional UX. They increasingly embrace Agile methodologies and DevOps practices to accelerate software development and enhance collaboration between development and operations teams. Agile and DevOps enable faster time-to-market, improved quality and increased flexibility in responding to changing business needs. Enterprises increasingly focus on cloud engagements and infrastructure modernization. They further concentrate on transitioning their applications to cloud-native architectures, leveraging containerization and microservices. This shift allows for greater scalability, resilience and agility in deploying and managing applications.

Application managed services: Most enterprises attempt to optimize cost and efficiency in managing applications within their IT landscape. Service providers have devised methodologies and approaches to use technologies such as AI, automation and analytics to deliver tangible benefits to clients. Data-driven approaches deliver better experiences and adhere to the agreedupon KPIs. As cloud adoption increases, the need to manage cloud applications and optimize infrastructure availability and application performance becomes essential. Service providers align with market expectations and leverage AI — to a certain extent, generative AI — to deliver application managed services to their clients.

Application quality assurance: In today’s rapidly evolving development landscape, organizations face shorter software release cycles, necessitating customized testing solutions and adopting DevOps practices and tools. Service providers’ quality assurance (QA) practices focus on achieving exceptional UX. Providers also emphasize the development of cloud-based automation platforms that leverage AI and ML to address enterprise demands for faster cycles. These testing platforms use codeless, self-healing and predictive test automation to enable a faster and more efficient software delivery. The increased demand for shift-left testing approaches using AI and ML for test automation has helped improve application quality. Using SRE to improve application quality before deployment into the production environment, thus reducing application downtime, is also an emerging trend.

Continuous testing specialists: Enterprises focus on leveraging cloud-based testing and ADM services to enhance scalability, flexibility and cost-effectiveness as the adoption of cloud applications increases. This engagement enables enterprises to simulate real-world scenarios and perform comprehensive testing across different platforms. With the intense pressure to deliver efficient applications, leveraging AI and automation has become a mandate for service providers. This setup enables providers to use AI-powered testing and automation to help clients identify patterns, predict potential defects and optimize test cases. Advanced generative AI techniques are used for AI-led software testing. Firms prioritize security testing and compliance owing to the increasing number of cyber threats and data breaches. They incorporate robust security testing practices, including vulnerability assessments, penetration testing and code analysis.

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