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In the field of app testing, QA managers and other senior testing professionals must implement different test management approaches such as test monitoring and control to ensure that the test suite runs smoothly. These essential management strategies are required by the managers to track and align the test progress for optimal accuracy and efficiency.
Test monitoring is a test execution process in which all testing activities and efforts are evaluated to:
Quite often we are consulted to design a robust test strategy for a mission-critical enterprise chatbot. How is it possible to test something for all possible unexpected user behaviour in the future? How can someone confidently make assumptions on the quality if we have no clue what the users will ask the chatbot?
While we do not own a magic crystal ball to look into future usage scenarios, from our experience we gained the best results with a systematic approach in a continuous feedback setup. In almost every chatbot project the use cases can be categorized:
A solid testing strategy is a key point to the successful adoption of agile development. Test coverage is a metric used to measure how much of the source code of a program is executed by running a set of tests. It helps developers to identify the code in their application that was not tested.
Ideally, tests against software should define all behaviors of the software. However, this is rarely realized. That is how test coverage comes into play.
Test Coverage and Code coverage are the most popular methodologies for measuring the effectiveness of the code. Though these terms are sometimes used interchangeably since their underlying principles are the same. But they are not as similar as you may think.
Many times, I have noticed the testing team and development team being confused over the use of these two terminologies. This is why I thought of coming up with an article to talk about the differences between code coverage and test coverage in detail.
Organizations today have long understood the need to automate test execution, and 90% believe that automated testing allows testers to perform their tests quicker. Yet, QA teams are struggling to achieve sufficiently high rates of automated test execution. Slow and overly manual testing still abounds.
In 2018, 61% of organizations had automation rates lower than 50%. This article considers five reasons for these low rates of functional test automation, setting out some of the most common pitfalls to watch out for when adopting a test automation strategy.
I'm not a believer in having standards for test coverage. I know teams that require 60%, 70%, or 80% test coverage for their code. I don't like having standards like this because different code has different requirements for testing.
Straightforward code like getters and setters don't really need to be tested. However, more complex code that encapsulates business rules should be tested. When developers do "test after" software development by writing their tests after they write their code, they typically try to find the easiest code to test in order to meet their code coverage standards, but oftentimes this is not the code that we really need to have covered.
Product quality is more important to the success of businesses than ever before. In a software market flooded with an ever-increasing pool of options, businesses, and consumers alike demand high-quality, delightful experiences from the platforms and applications they use daily.
In this market, getting QA right is essential. For the 2019 QA Health Survey, we polled over 250 software professionals to get a sense of how healthy quality organizations are right now. Read on to see some of our key findings, and to download the full results of the survey.
The concept of mutation testing is to modify code in a small way and verify that tests detect that modification. Undetected modification gives a hint to what test is likely missing.
But why do we have to consider testing the result of the modified code? Let's take a look at an example: