Evolution is a given in any domain. Software testing and quality engineering have a very key role in the product development landscape in safe guarding user interests and ensuring a product of exceptional quality is released. When this function holds such an important and dynamic charter to deliver on, it is obviously inevitable that it continues to evolve to ensure the efforts are smart, nimble and adaptable to the changing requirements.
Besides the core software testing strategy and practices that have been traditionally followed both manually and in automated manner, there is a lot that the function is picking from and will need to pick from the current industry scenario. For example, even in automation, open source offerings and opportunities are limitless. The community is rich and forthcoming in helping each other solve complex scenarios and enhancing test coverage. The latest evolutions in technology especially around AI, are offering a lot to the testing community, enabling us develop smart software testing approaches, frameworks and solutions. This is not limited to just functional testing but even non-functional test areas. For example, at QA InfoTech, we have ongoing investments in leveraging AI in software testing solutions for areas including accessibility testing, security testing, performance testing besides core functional testing and automated solutions therein. A couple of our testing frameworks, for instance, in the accessibility space, leveraging image recognition capabilities, natural language processing and machine learning have enabled us make huge strides in testing for the support for differently abled in a fast and reliable manner. These have also helped us truly shift quality left giving developers solutions in building quality upfront.
Similarly, while organizations typically are very cautious about taking up any activity in production, for the right reasons, when handled diligently, production offers a very rich set of data and information in further strengthening the current quality index, reducing the test debt and improving the overall health of the product and market acceptance. Until a few years back it was mainly around minimal software testing in production, providing support, and ongoing monitoring to understand what kind of issues are encountered to learn from them. They were all largely still reactive in nature. Technology evolutions have given us an advantage here as well, where through machine learning one is able to easily and effectively parse through large data sets to learn from usage patterns, the application’s performance, what is going well and what isn’t, monitor and learn from competitors, all in the true spirit of proactively focusing on improved solutions and quality, on an ongoing basis. Even using live user data can now be done in a controlled and safe manner, making production a very rich environment to learn from.
All of this is however just a start or rather an ongoing process. While it is AI and testing in production that is giving organizations an edge today in software testing differentiating one’s test strategy from another, tomorrow it would be something else. Such evolutions certainly help organizations and their quality efforts stay current, avoid complacency and more importantly benefit end users in getting solutions of top notch quality, not compromising on other attributes such as time to market and cost.