Editor’s note: Whether you fail to keep up with a project deadline or suffer from low-quality software, our QA consultants can help you manage these issues with a comprehensive QA process audit and redesign. In the article below, we have outlined best practices that have helped our customers improve QA process and reduce the number of defects in production while meeting project deadlines.
During 19 years of our QA outsourcing and consulting practice, we have helped many customers who decided to outsource QA to ScienceSoft solve their quality problems by redesigning their initially unstructured QA process.
In this article, we have summarized six best practices, which help us improve QA process for our customers. You can adopt them throughout the software development life cycle to resolve your quality issues and make your QA process more efficient.
When it comes to product backlog refinement, quality assurance team members can be of great help as they are usually good at understanding business requirements from end users’ perspective. At the same time, they know an application’s technical dependencies very well. So, to eliminate unclear and contradicting requirements and improve the end user experience, you should involve the QA team members in the clarification and prioritization of the backlog items and let them check the already available and newly created requirements and user stories for clarity, consistency, testability, and traceability.
Regression testing can take up to 70% of testing efforts. Therefore, optimizing regression management is the primary step towards a shorter release cycle. ScienceSoft advises planning potential regression at the start of a sprint or iteration. For that, identify software functionality that is likely to be affected by each new requirement/user story in the sprint backlog. Once you and your QA team have acquired an understanding of the software modules that can experience regression, make sure the QA team prepares and updates test design documentation covering these modules. This way, QA engineers won’t have to run a full regression test suit to ensure sufficient test coverage.
Test automation can speed up the software testing process and substantially improve software quality. However, a test automation initiative will only pay off if the efforts spent on design, development and support of test automation scripts and a corresponding environment are lower than the efforts spent on manual test execution. Another condition for test automation success is careful selection of automation candidates. While you can easily automate those test cases that are executed frequently and do not change much with each iteration, the automation of complex or frequently changing test cases requires much effort, which often outweighs the promised benefits.
If you work on software that has a critical dependency on data, for instance, a BI application, we recommend that your QA team designs a test data management strategy early on. For that, let the QA team formulate data requirements – the volume of the required data set, the data format, and boundary values – and based on them, identify an optimal approach to test data generation:
- Cloning the data from production: the method is relatively simple to implement, however it requires much time due to usually large production data volumes.
- Generating synthetic test data: the method guarantees that no sensitive production data is disclosed, but it is resource-, effort-, and time-intensive.
- Sub-setting the data from production: the method is less costly compared to the previous two, but it requires skilled resources to ensure data integrity.
For the sake of testing quality, each meaningful change introduced to an application should be reflected in test design documentation. Therefore, at the start of each sprint, have your QA team assess how the changes introduced to the system affected the existing test design documents, remove irrelevant and duplicate test cases, and identify which test cases need to be updated.
Take software testing activities closer to the beginning of a sprint, as it helps identify defects at the point when they require considerably less time and effort to fix. To realize the shift-left approach, involve the development team in the quality assurance process and have them perform regular code reviews and unit testing. Then, let the test team take over and perform manual and automated testing activities at the application’s API and UI levels.
Advance your quality assurance process
A mature quality assurance process tailored to the project needs is a guarantee of on-time releases and high-quality software. Redesigning the established QA process, however, requires substantial QA expertise and is time- and effort-intensive. ScienceSoft is ready to support you in QA process improvement and perform QA process audit and redesign.