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Exploratory testing (ET) is a powerful and efficient way of testing software by integrating design, execution, and analysis of tests during a testing session. ET is often contrasted with scripted testing, and seen as a choice between black and white. We pose that there are different levels of exploratory testing from fully exploratory to fully scripted and propose a scale for the degree of exploration for ET. The degree is defined through levels of ET, which correspond to the way test charters are formulated. We have evaluated the classification through focus groups at four companies and identified factors that influence the level of exploratory testing. The results show that the proposed ET levels have distinguishing characteristics and that the levels can be used as a guide to structure test charters. Our study also indicates that applying a combination of ET levels can be beneficial in achieving effective testing.
Context: Internal chemical mixing in intermediate- and high-mass stars represents an immense uncertainty in stellar evolution models.In addition to extending the main-sequence lifetime, chemical mixing also appreciably increases the mass of the stell
Discussions is a new feature of GitHub for asking questions or discussing topics outside of specific Issues or Pull Requests. Before being available to all projects in December 2020, it had been tested on selected open source software projects. To un
Todays cloud service architectures follow a one size fits all deployment strategy where the same service version instantiation is provided to the end users. However, consumers are broad and different applications have different accuracy and responsiv
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affecte
In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i.e., the learners reward performance must be at least as well as a given baseline at any time. We propose a One-Size-Fits-All solution to CB