Throughout my career, my research has focused on creating and evaluating methods for automatically generating test cases and test inputs, thereby supporting programmers with clever tests that may find defects in, for instance, their programs or database schemas. As an example, (Alsharif2018) introduces a tool for automatically generating tests for relational database schemas and (McMinn2016b) presents a framework that supports the creation of automated test data generation tools for the Java programming language. However, until recently, none of my research papers assessed whether or not these approaches actually help human testers. Of course, my colleagues and I recognized that this was an area that warranted further investigation! I'm pleased to report that our recent paper, (Alsharif2019) ,  presents a human study of automated test data generation techniques for relational database schemas.
Since relational databases are a key component of software systems ranging from small mobile to large enterprise applications, there are well-studied methods that automatically generate test cases for database-related functionality. As explained in a previous blog post called How do you test a relational database's schema?, a schema testing tool automatically generates
INSERT statements with data values designed to either satisfy (i.e., be accepted into the database) or violate the schema (i.e., be rejected from the database). From my own experience, writing schema tests is difficult because a human tester has to carefully reason about the, sometimes complex, relational schema that protects the database's contents. While having tools like SchemaAnalyst (McMinn2016c) ,  to automatically generate tests is useful, as I mentioned previously, there has been no research to analyze how well testers both understand tests involving SQL and decide if they reveal flaws.
The aforementioned paper, (Alsharif2019) ,  reports on a human study of test comprehension in the context of automatically generated tests, created by SchemaAnalyst, that assess the correct specification of the integrity constraints in a relational database schema. The study reveals two key findings. First, the choice of data values in
INSERTs influences human understandability: the use of default values for elements not involved in the test (but necessary for adhering to SQL’s syntax rules) aided participants, allowing them to easily identify and understand the important test values. Yet, negative numbers and "garbage" strings hindered this process. The second finding is more far reaching: humans found the outcome of test cases very difficult to predict when
NULL was used in conjunction with foreign keys and
CHECK constraints. This suggests that, while including
NULLs can surface the confusing semantics of database schemas, their use makes tests less understandable for humans. While these results specifically apply to database schema test, we envision that the results largely apply to automatically generated tests for programs.
Ultimately, my co-authors and I make the following suggestions for both the software engineers who manually write tests and for developers who create tools that automatically generate tests.
Negative numbers and
NULL values are confusing for human testers and tests should only include them when they are specifically needed to reveal a defect.
Test should use simple repetitions of numerical, categorical, and textual values for unimportant values because they make it easier for human testers to focus on the critical values on which the test’s status hinges.
Since readable strings, in comparison to random textual values, help to ensure that human testers better understand the intention of the test case, they should be used whenever possible.
While some automated test data generation methods are starting to adopt these suggestions, more work remains to create automated test data generation tools and manual testing guidelines that ensure that tests are understandable by human testers. Do you have ideas about how to make tests more human readable? Are you interested in collaborating with me on research to make the next generation of automated test data generation techniques? If so, then I hope that you will contact me to share some of your ideas! Or, do you want to be updated when I publish new blog posts like this one? If you do, then please subscribe to my mailing list.
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