Okay, so that title doesn’t make complete sense. However, if you read to the end of this article, all will become clear. I’m first going to discuss some of the persistent barriers to in-sprint testing and development. I will then discuss a viable route to delivering rigorously tested systems in short sprints.
The two kingpins in this approach will be data and automation, working in tandem to convert insights about what needs testing into rigorous automated tests. But first, let’s consider why it remains so challenging to design, develop and test in-sprint.
5 Reasons to Model During QA, Part 5: A “Single Pane of Glass” For Technologies and Teams
Welcome to the final installment of 5 Reasons to Model During QA! If you have missed any of the previous four articles, jump back in to find out how modelling can:
- Identify bugs during the requirements analysis and design phase, where they require far less time and cost to fix;
- Drive up testing efficiency, automating the creation of test cases, test data and automated test scripts;
- Maximise test coverage and shorten test cycles, focusing QA on the most critical, high risk functionality;
- Introduce QA Resilience and Flexibility to change, automatically updating a rigorous test suite as requirements evolve.
This last article in the series shifts focuses to consider modeling within the broader context of the Software Delivery Lifecycle. It goes beyond QA, considering how models deliver value to the BAs, developers, and testers who can work collaboratively from them.
Scriptless Testing Is Not Just Record and Playback: Top 10 Scriptless Testing Approaches
In traditional software development, testing professionals manually tested the developed software but the need to test redundant scenarios necessitated the use of testing tools that would allow them to execute the same tasks automatically.
The benefit of automated testing was a reduced time to test legacy test scenarios, while the downside was that test automation involved the use of scripts. Testers needed to either learn the supported languages for automation testing or hire new resources who knew to code.
5 Reasons to Model During QA, Part 1/5: “Shift Left” QA Uproots Design Defects
Model-Based Testing (MBT) itself is not new, but Model-Based Test Automation is experiencing a resurgence in adoption. Model-Based Testing is the automation technique with the greatest current business interest according to the 2018 World Quality Report, with 61% of respondents stating that they can foresee their organization adopting it in the coming year. [1]
Technologies like The VIP Test Modeler have significantly reduced the time and technical knowledge needed to model complex systems. Organizations can now enjoy all the benefits of Model-Based techniques within short iterations, whereas previously modeling had been reserved for only the most high-stake projects, such as lengthy Waterfall projects in aerospace and defense.
Enterprise-Grade QA on a Budget
We operate in a continuous delivery world in which a seamless customer experience is paramount. Regardless of whether you're a global Fortune 500 organization or a fast-growing startup, failing to deliver a digital experience that delights your users is a critical mistake you can't afford to make.
A chief challenge compounding today's continuous delivery expectation is the growing amount of testing that has to be carried out. In the not-too-distant past, companies controlled all of their software, available on a single platform to a similar type of user with one uniform release cycle. Today's landscape is vastly different, with websites and apps relying on a mix of modules and services under the control of various vendors, all with independent release cycles, in a heterogeneous platform environment with a wide range of user types.