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Quality Engineering Is Not Just End-to-end Software Testing
The fast-changing dynamics of the global digital ecosystem involve quick adoption of advanced technologies, faster time-to-market, seamless rendering of workflow, and the delivery of top-notch product quality. Should any enterprise flounder in providing the above-mentioned outcomes, the competitors will ultimately have the last laugh. Also, enterprises are facing challenges arising out of mergers and acquisitions, risk and compliance, data and analytics among others. These activities are kindling the expectations of stakeholders (including end customers) to a whole new level. No wonder enterprises are looking for ways and means to stay on the right side of competition. The focus is exclusively on enhancing the quality of products and services using methodologies like Agile and DevOps. However, the reshaping of customer expectations and the prevalence of intense competition in the digital ecosystem, businesses can no longer rely on the efficacy of shift-left quality assurance alone. They must make a gradual shift towards quality engineering.
What is Quality Engineering and how it differs from Quality Assurance?
If quality assurance is about identifying the defects in a software application or system using the shift-left or waterfall model of testing, software quality engineering is about transforming the product workflow, processes, and team culture to pre-empt the ingress of glitches in the first place. So, if QA is about identifying and removing glitches, QE is about reshaping the workflow to prevent the generation of glitches. The latter encompasses methodologies that have an omnichannel approach straddling multiple engineering disciplines. In software quality engineering, the activities involve the designing, development and manufacturing of products to enhance their quality as well as reduce waste along the way.
The quality engineering approach is not just about following end-to-end software testing as in a typical QA process. It is about defining, analysing, and implementing technologies, methods, and tools that are in tune with the business objectives of enterprises. The software quality engineering services may involve the following key activities.
Automation of testing processes: When it comes to the quick delivery of products/services to beat the competition and garner customer satisfaction, the existing testing processes need to be automated. Here, AI can be leveraged to analyse the glitch prone sections of software as well as to suggest correctives. Test automation can enhance conventional test strategies through AI and achieve continuous integration and delivery – noted outcomes of DevOps methodology. Test automation can leverage both AI and Machine Learning (AI & ML) to optimize testing.
Strengthen shift-right testing: As businesses go about developing and deploying quality applications, elements like mobile commerce, social, IoT adoption and cloud are making QA shift left and strengthen right. The comprehensive QE initiative would involve activities like virtualization, test-driven-development, API testing, performance engineering, security testing, and analytics-driven automated testing. Since the ultimate objective of enterprises is to develop quality products that can derive customer delight, shift-right testing helps to validate the quality of software from the right – post production. This test approach focuses on the performance and usability of the software and in the process, seeks continuous feedback from customers to understand their behaviour.
Achieve agility: Agile and DevOps have emerged as key enablers for business enterprises in a software development life cycle. The effective implementation of digital quality engineering by leveraging industry best practices can help businesses achieve quality outcomes across digital touchpoints. The Agile-led processes can identify and mitigate glitches besides integrating the various software modules faster to deliver customer delight.
Value proposition: The software QE services test and analyze the entire value chain thereby looking for inherent vulnerabilities or defects. These services ensure the value chain is compliant with the regulatory requirements and has the necessary wherewithal to detect and eliminate glitches. Also, there is a renewed focus on offering customer-centric innovative solutions and enhancing the value proposition. These objectives are achieved through minimal testing efforts and encompass the reduction in production glitches. As quality engineering aims at improving the test effectiveness of enterprises, the quality assurance of product/service gets enhanced leading to its rising acceptability among customers.
With the automation of workflow including the regression suite, there is an increased adoption of mature practices and testing methodologies. The implementation of quality engineering over quality assurance by using tools like 4D analytics, Artificial Intelligence, and Machine Learning can lead to the optimization of test cases and better prediction/identification/mitigation of data.
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