Snapshot testing is a form of software testing that is focused on visual components by highlighting any code changes when compared to a previously stored state. This quick and simple method of testing is growing popular among the industry with companies such as Spotify and Robinhood. Despite its growing popularity, snapshot testing is barely explored in academia. In this paper, we use GitHub API to collect a dataset of 686 repositories tagged with Jest, a popular testing framework capable of snapshot testing. From those repositories, we found 4,604 snapshot files and 11,367 test files. The top-10 repositories represent 20% of all snapshot files in the dataset, even though it is only 3% of the size. We acknowledge that improvements can be made in the dataset but due to the lack of data on snapshot testing, we believe the current dataset is useful in helping researchers to study this topic.

Software testing enables development teams to maintain the quality of a software system while it evolves. The database manipulation code requires special attention in this context. However, it is often neglected and suffers from software maintenance problems. In this paper, we study the current state-of-the-practice in testing database manipulation code. We first analysed the tests of 72 open-source projects to gain insight into the coverage of database access code. The database was poorly tested: 46% of the projects did not cover with tests half of their database access methods, and 33% did not cover the database code at all. This poor coverage motivated us to study developers’ challenges and best practices. (i) First, we analysed 532 questions on Stack Exchange sites and deduced a taxonomy of issues. Developers mostly looked for general best practices to test database access code. Their technical questions were related to database management, mocking, parallelisation, or framework/tool usage. (ii) Next, we examined the answers to these questions. We manually labelled 598 answers to 255 questions. We distinguished 363 solutions and organised them in a taxonomy of best practices. Most of the suggestions considered the testing environment and recommended various tools or configurations. The second largest category was database management, where many addressed database initialisation and clean-up between tests. Other categories pertained to code structure or design, concepts, performance, processes, test characteristics, test code, and mocking. We illustrate the two taxonomies through intriguing examples.