Top 7 software testing trends to watch out for in 2021

2021年值得关注的7大软件测试趋势

2021-04-11 09:08 RWS Moravia Insights

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The rise of artificial intelligence and machine learning in testing Utilizing AI algorithms to identify the test cases that need both manual and automated testing; Optimizing test suites by determining and eradicating unnecessary test cases; Ensuring optimal test coverage to identify essential keywords from a Requirements Traceability Matrix; Predicting key parameters and metrics which are defining end-user behaviour and identifying areas to focus on; and Detecting the application areas and any defects associated with business risks. The significance of QAOps development Testing u0026amp; QA activities execution with CI/CD, aiming to achieve high quality and quick delivery. QA engineers working in parallel with the development team for faster speed-to-market. Internet of things (IoT) testing to boost digitally connected smart devices The increasing demand for big data testing The incorporation of QA test automation Codeless test automation: these testing tools are built using AI and visual modelling to facilitate the automation of testing scenarios without requiring any coding knowledge. Robotic process automation testing: this involves automation testing tools that help testers replace regression and load testing, massively reducing the time and manual input requirements; it’s particularly useful for dealing with large and complex data sets. Blend of agile and DevOps: this type of automation testing provides high test coverage for agile teams, aiding businesses in faster deployments and cost management. A shift towards performance engineering from performance testing Cyber security testing and risk compliance
人工智能和机器学习在测试中的兴起 利用人工智能算法识别既需要人工测试又需要自动测试的测试用例; 通过确定和消除不必要的测试用例来优化测试套件; 从需求跟踪矩阵中确定关键字的最优测试复盖率; 预测确定最终用户行为和确定重点领域的关键参数和衡量标准;和 检测与业务风险相关的应用领域和任何缺陷。 QAOps发展的意义 测试U0026amp;与CI/CD一起执行QA活动,以实现高质量和快速交货为目标。 QA工程师与开发团队并行工作,以加快产品上市速度。 物联网(IoT)测试促进数字连接智能设备 不断增长的大数据测试需求 QA测试自动化的整合 无代码测试自动化:这些测试工具是使用AI和可视化建模构建的,以方便测试场景的自动化,而不需要任何编码知识。 机器人过程自动化测试:这涉及自动化测试工具,帮助测试人员替代回归和负载测试,大量减少时间和手动输入需求;它对于处理大型和复杂的数据集特别有用。 敏捷和DevOps的融合:这种类型的自动化测试为敏捷团队提供了很高的测试覆盖率,帮助企业进行更快的部署和成本管理。 从性能测试转向性能工程 网络安全测试与风险合规

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