Deliver high-quality software faster and more efficiently with Publicis Sapient and Google Cloud
Quality engineering (QE) plays a critical role in delivering the fast, convenient, and reliable digital experiences that app users expect today. While organizations previously left software testing to the end of the development cycle, QE takes a proactive approach: DevOps teams conduct testing and address potential defects throughout application development. This shift from more reactive quality assurance (QA) practices to QE has enabled DevOps teams to speed time-to-market, improve product quality, reduce security risks, and eliminate the high costs of corrective measures.
While QE helps overcome key limitations of traditional software testing, it can create new challenges for DevOps teams. As application complexity grows, QE can become increasingly time-consuming and resource-intensive.
With the right AI tools and expertise, organizations can transform their QE processes to rapidly and efficiently deliver high-quality software. This playbook outlines how Publicis Sapient, a Premier Google Cloud Partner, leverages Google Cloud’s comprehensive suite of AI-powered services, and the expertise of Publicis Sapient, to empower DevOps teams in adopting AI for Test Automation and QE. The goal is to maximize the benefits of QE by automating testing, enhancing detection of issues, and suggesting fixes—solving potential problems in less time and with less effort than using manual processes.
DevOps teams often encounter multiple challenges as they implement QE processes across the software development lifecycle. If they fail to address these challenges, the benefits of QE will begin to evaporate.
DevOps teams are under tremendous pressure to deliver new applications, innovative features, and bug fixes as rapidly as possible. QE can save time in the long run by addressing potential issues early in the development process. But the growing complexity of applications adds time to testing and debugging.
Whether they are using applications for e-commerce, healthcare, banking, entertainment, or government services, users want software to be responsive, convenient, and engaging. When new devices, operating systems, or AI capabilities become available, many people expect all of their applications to be immediately updated. For DevOps teams, these rigorous demands and shifting expectations place additional pressure to deliver new application features and digital experiences rapidly—which requires teams to complete QE processes faster.
Manually designing tests, running tests, and addressing defects can be extremely resource-intensive, especially as teams strive to continuously deliver new features. Many organizations lack sufficient personnel with appropriate skills for all this work, let alone the budget to buy and maintain testing infrastructure. Even the largest DevOps teams can find it difficult to scale QE as demands for accelerating development intensify.
Modern applications are often complex systems. The use of microservices architectures, integration with multiple cloud-native endpoints using APIs, and the incorporation of advanced analytics or AI capabilities help make applications more portable, agile, and engaging. But these techniques and technologies complicate QE: teams need to test and monitor more components, in more places, than with traditional monolithic applications.
Incorporating AI into development can help address these challenges. By applying AI-driven automation in QE, teams can accelerate processes, accommodate shifting requirements, enhance the efficiency of testing, and minimize the impact of complex architectures. For many teams, the first step in benefiting from AI in QE is to choose the use cases that will have the greatest impact for their processes.
Many software development teams are eager to incorporate AI capabilities into their processes. They recognize that AI tools can help boost the productivity and efficiency of their work across several phases of development, including code generation, refactoring, documentation, testing, and more. According to one academic study, using a generative AI coding assistant for development can increase productivity by more than 26%.
AI can be used in multiple ways to streamline testing and QE processes. Many DevOps teams benefit from prioritizing use cases as they plan to integrate AI into QE.
Test cases—which evaluate the effectiveness of specific app features or functions—are crucial for software validation. DevOps teams can use AI to analyze code, requirements, and past test results, and then automatically generate new test cases, including edge cases that might be missed by human testers. More than half of developers surveyed by IDC (51.2%) are already using AI coding assistants for unit test case generation, while 41.2% of developers are using AI for code generation.
Unit tests verify the accuracy of small blocks of app code, helping to ensure each code block runs as intended. By testing small blocks, teams can find and address potential problem areas early, rather than trying to identify defects later within complicated architectures. Using AI for unit tests can reduce the time-consuming, tedious work of writing tests and reviewing results. Consequently, teams can speed workflows while eliminating errors.
Test scripts help assess whether applications are producing expected outcomes. Employing AI for automation test scripts can accelerate testing and improve software reliability. AI can help with test script maintenance by automatically updating and maintaining test scripts as the software evolves, reducing the time and effort to keep tests current.
AI tools can predict potential bugs, identifying areas of code that are most likely to contain errors or defects. These tools can spot anomalies in system behavior, which might indicate potential code problems and quickly determine root causes. In response, AI-based code assistants can suggest code fixes, again saving developers’ time while improving software quality.
Built on state-of-the-art technologies, Google Cloud offers comprehensive AI-powered services that enable DevOps teams to take full advantage of AI for QE across the development lifecycle.
Vertex AI is a fully managed, unified machine learning (ML) and AI platform for building and using GenAI in development processes. It comprises:
Teams can employ Vertex AI to generate test cases and unit tests, create automation test scripts, and debug code. To produce a test case, for example, a team would input descriptions of the features or functions they want to test into an AI model. The model would then analyze the input and generate a set of test cases, along with detailed descriptions of inputs, expected outputs, and potential failure scenarios.
Gemini Code Assist helps increase the velocity of software development and delivery by providing AI-driven assistance for coding. The tools can complete code as a developer writes, even generating entire code blocks or functions on demand.
For QE processes, Gemini Code Assist can help developers find and fix code errors. Debugging is as simple as highlighting specific code and prompting the tool to help debug the code. Gemini Code Assist can automatically write unit tests and provide explanations of functions in code.
Google Cloud also offers a wide range of additional services that can help developers rapidly deliver high-quality code, improve test coverage, and increase the efficiency of testing. For example:
Google Cloud offers a wealth of AI tools and services that can be applied to top QE use cases. But some organizations still face obstacles slowing implementation. According to one survey, 41 percent of organizations report that lack of employee expertise is their biggest challenge for implementing Gen AI.
Publicis Sapient is a Premier Google Cloud Partner who can help organizations streamline the implementation of AI tools and services from Google Cloud for QE processes. As a highly experienced Google Cloud partner organization, Publicis Sapient offers a best-in-class team that enables organizations to move forward on their AI journey. Through the partnership with Google, Publicis Sapient has built a dedicated Google Cloud business unit to address demand for Google Cloud’s AI technology.
To simplify implementation of AI for all software development tasks, Publicis Sapient created the Sapient AI for Applications suite of solutions. DevOps teams can use these solutions not only for test automation and QE, but also for application modernization, data modernization, and custom application development. The solutions bring together an AI-assisted agile engineering process, more than 2,500 experienced people, and a proprietary AI platform, Sapient Slingshot, that Publicis Sapient uses to help accelerate the entire software development lifecycle.
Sapient Slingshot drives enhancements in productivity, efficiency, speed, and quality. Blending advanced code generation with agentic AI and an enterprise-level code library, our engineers use Sapient Slingshot to create high-impact solutions for your business—supporting everything from modernization to development and testing. The platform includes a chat-based interface, enabling simple prompting and streamlined output generation. Sapient Slingshot features various AI agents that can automate complex development processes, drawing from a Publicis Sapient code repository to deliver industry-specific solutions rooted in our extensive client work.
When organizations need to focus on QE, the Publicis Sapient team builds a modular testing framework that adapts to client quality and development engineering processes. That framework can employ a full spectrum of AI tools from Google Cloud as part of AI-led automation and validation services.
By combining Publicis Sapient expertise with Google Cloud’s AI tools, DevOps teams can work better together to address the primary challenges of QE processes:
Ultimately, teams can accelerate time-to-market for high-quality software that meets the demanding requirements of users.
The QE process is vital to delivering robust, reliable, and secure applications that meet users’ evolving expectations. Yet manual QE processes can be time-consuming and costly, especially as the complexity of application architectures increases.
Implementing AI capabilities from Google Cloud, like Google Cloud Run and Google Cloud Observability, can help transform QE. With help from expert teams from Publicis Sapient and the Sapient Slingshot platform, organizations can implement those capabilities as part of a tailored framework. As a result, organizations can deliver higher-quality software even faster and more efficiently than before.
For more information, visit publicissapient.com/partnerships/google.
Sharonyka Kumar
Group Vice President, Global Head of Google Business Unit, Publicis Sapient
sharonyka.kumar@publicissapient.com
Publicis Sapient is a digital transformation partner helping established organizations get to their future, digitally enabled state, both in the way they work and the way they serve their customers. We help unlock value through a start-up mindset and modern methods, fusing strategy, consulting, and customer experience with agile engineering and problem-solving creativity. As digital pioneers with 20,000 people and 53 offices around the globe, our experience spanning technology, data sciences, consulting, and customer obsession—combined with our culture of curiosity and relentlessness—enables us to accelerate our clients’ businesses through designing the products and services their customers truly value. Publicis Sapient is the digital business transformation hub of Publicis Groupe. For more information, visit publicissapient.com.
Google Cloud is the new way to the cloud, providing AI, infrastructure, developer, data, security, and collaboration tools built for today and tomorrow. Google Cloud offers a powerful, fully integrated, and optimized AI stack with its own planet-scale infrastructure, custom-built chips, generative AI models and development platform, as well as AI-powered applications, to help organizations transform. Customers in more than 200 countries and territories turn to Google Cloud as their trusted technology partner. For more information, visit cloud.google.com.