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Software Quality Assurance: Building AI-driven competencies

With advancements in various technologies, be it AI & Gen AI, RPA, LCNC, IaC, etc., the need for speed, scale and quality are factors defining the development & deployment of software applications. Quality Assurance (software testing), an integral part of SDLC, has been going through its own automation journey from manual testing to functional automation to smart automation and now, Intelligent and Codeless automation

This whitepaper, an outcome of the webinar we organized on The Gen A(I)ge & Software Quality Assurance – IMPLICATIONS, aims to showcase how this shift is changing the role of the testers, how their role will evolve around AI & Gen AI and hence, the additional skillsets required.

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Key Findings

Introduction
  • Various automation trends and emerging technologies especially AI & Gen AI are shaping the Quality Assurance segment and consequently, the role of testers/quality engineers is changing
Trends
  • QAOps - Deeper convergence of coding, testing and design
  • AI & Gen AI: Automating test case & test data generation, unit tests, and test scripts
  • Cloud-native testing: Quality assurance of solutions built for the cloud
  • Emerging areas: Visual testing, API testing and Accessibility testin
Emerging and evolving job roles
  • Data Intelligence Analysts
  • AI Quality Engineers, QAOps Engineers
  • Voice AI Specialists
  • AI/Gen AI Test Automation Engineers
  • Test Architects
  • Existing roles that need new skills include QA Managers, ML model Validation & Monitoring, Exploratory Testing and SDETs
Role of humans
  • Inspite of various automation trends, human supervision will continue to be critical at every stage of the Quality Assurance lifecycle – building the relevant context, identifying hallucinations and AI bias, validating AI-generated code, ensuring security and compliance, to name a few

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