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AI’s Impact on Quality Engineering of Modern Digital Products
AI’s Impact on Quality Engineering of Modern Digital Products

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As Fabrizio Biscotti, Gartner’s Vice President for Research, said, “Hyperautomation has shifted from an option to a condition of survival”. As technology marches forward, intelligent automation use-cases are sprouting up across various domains. They can be found in everything from enterprise workflows to the swift-paced processes of software development in the race to market.

 

The landscape of software development has undergone a significant transformation due to Agile development, DevOps, and continuous advancements in technology. This evolution has led to monumental changes in how software product idea are engineered and deployed. Among the affected teams are Software Development Engineer in Test (SDET) teams, who now bear the pressure to achieve rapid and frequent releases.

 

Additionally, independent software vendors (ISVs) are actively strategizing their transition towards a stage where hyper-automation will revolutionize the entire product development process.

 

However, when we encounter the speed limit imposed by conventional development practices, independent software vendors (ISVs) must turn to Artificial Intelligence (AI) and Machine Learning (ML) technologies. By doing so, they can foster the emergence of autonomous processes and development paradigms, allowing for even more remarkable acceleration. Embracing AI in the Quality Assurance (QA) practice can be a significant step on the journey towards creating superior digital products at a faster pace, providing additional value.

 

AI's Role in Tackling Unique Quality Engineering Challenges in Product Vision and Development

 

In the realm of product development, as the pace accelerates exponentially, novel quality engineering challenges emerge, calling for AI-powered solutions. Some of these key challenges encompass:

 

  • Predicting defect patterns to optimize product management and development, leading to reduced project costs.
  • Facilitating better forecasting of QA costs by leveraging project sizes, improved effort estimation, and transparent budgeting or billing for future projects.
  • Creating highly reusable test cases and test data to expedite time-to-market, product-to-market, and minimize testing expenses. 

 

Leveraging AI for Seamless Feedback Integration in Digital Product Development to Enhance Customer Satisfaction

 

Quality Assurance (QA) holds a pivotal position in ensuring that digital products fulfill customer expectations across various use-case scenarios and target audience. In this dynamic landscape, the integration of AI-driven intelligent testing presents an appealing path forward.

 

AI-Driven Proactive Quality Assurance

 

The realm of artificial intelligence and machine learning has the capacity to learn from extensive datasets of successful code. These algorithms prove invaluable in assessing ongoing coding exercises by identifying deviations from established norms, particularly in similar functionalities or user scenarios. These AI systems evaluate the extent of these deviations and promptly alert Software Development Engineer in Tests (SDETs) when these deviations are likely to lead to quality concerns.

 

By analyzing historical data sets containing information about root causes, occurrence patterns, severity, market research and impact, software development teams harness AI's potential to minimize defects in future projects. AI-infused testing can predict potential defect-prone modules or test scenarios based on metrics like Defect Removal Efficiency (DRE) and Defect Rejection Ratio (DRR). This predictive insight directs attention to critical areas, aiding in defect prevention.

 

Enhanced Quality and Precise Estimations Through AI-Powered Quality Engineering

 

Leveraging AI's rapid data analysis capabilities, software engineering teams can achieve more accurate budgeting for development and testing efforts. AI's data-driven insights enable Engineering VPs to gauge the time and effort required to deliver a product of desired quality. Transparent budgeting and billing processes are established through AI's examination of historical data from similar projects, enhancing organizational planning.

 

Elevating Testing Efficiency and Fidelity with Artificial Intelligence

 

Although automation tools have facilitated test case automation, the complexity of large-scale products necessitates more robust solutions. AI algorithms can operate continuously and identify issues within complex enterprise applications, focusing on integrations and interfaces. Quality engineers benefit from comprehensive and accurate analysis, enabling strategic prioritization to expedite certification, validation, and time-to-market for intricate software products.

 

Intelligent and Adaptive Test Scripts

 

The advent of AI introduces the concept of self-learning test scripts. Through machine learning, these quality systems understand and mimic human-created test cases, enhancing automation efficiency and coverage. These adaptive test scripts evolve autonomously, adapting and learning from analogous scenarios, resulting in faster launches and more effective testing.

 

Moreover, AI-driven conversational tools, such as chatbots, facilitate constant interaction with customer stakeholders and development teams. These tools capture and convey feedback accurately, enhancing the authenticity of recorded feedback and augmenting test automation agility.

 

Charting the Path Forward with Artificial Intelligence in Quality Engineering

 

Establishing a sustainable software product and marketing strategy inherently entails crafting an efficient testing and quality assurance approach. In today's rapidly evolving landscape of digital products and applications, Independent Software Vendors (ISVs) are compelled to expedite their product roadmap. Leveraging artificial intelligence can empower ISVs to devise robust and scalable quality assurance methodologies, ensuring the rapid release of high-quality digital products that align with the pace of contemporary business demands.

 


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Xoriant is a Silicon Valley-headquartered digital product engineering, software development, and technology services firm with offices in the USA,UK, Ireland, Mexico, Canada and Asia. From startups to the Fortune 100, we deliver innovative solutions, accelerating time to market and ensuring our clients' competitiveness in industries like BFSI, High Tech, Healthcare, Manufacturing and Retail. Across all our technology focus areas-digital product engineering, DevOps, cloud, infrastructure, and security, big data and analytics, data engineering, management and governance -every solution we develop benefits from our product engineering pedigree. It also includes successful methodologies, framework components, and accelerators for rapidly solving important client challenges. For 30 years and counting, we have taken great pride in our long-lasting, deep relationships with our clients.

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