Get In Touch With And KeepTesters By Ross Collard
T
his is one in a series of articles on using live data in testing. It categorizes and outlines test data patterns that
You Might Not Even Realize How Many Patterns You Currently Use— Understanding Them Can Simplify Your Job
18
• Software Test & Performance
performance testers use. The patterns emphasize enhancing the data to fit the test goals. The articles in the series overlap, so that each can stand alone. Performance testers with intermediate-to-advanced skills constitute the primary audience for this article. No specific technical knowledge is assumed (e.g., a working knowledge of a particular testing tool, or technology employed in the support environment). The content should also be useful for functional testers and for non-testers who manage performance test projects. The objective of the article series is to improve your performance testing through the smarter use of live data. The scope is broad rather than deep, and includes Web sites, client/server systems, mainframes, telecom networks, databases and real-time embedded devices. You can apply these ideas to many situations, but I do not include any samples of test code, nor detailed performance tuning instructions, etc.
An Introduction to Patterns The word “pattern” is defined here as a practice, model or blueprint. While repetition is central to the concept of patterns, so are learning and improving on them. In this article series, I describe more than 80 test data patterns, most of which are massaged or enhanced for a particular test purpose. However, I’ve never met a tester who tests with 80 distinct data sets. (After
scrounging around in their files they are often surprised at how many they find.) Practically, managing and using more than a few data patterns is unwieldy, so testers consolidate them into a single one or a small collection. Here I describe them as many individual patterns so that the purpose and mechanics of each is clearer. In addition, developing one test data set for many purposes raises the risks of losing visibility and inadvertently introducing conflicts into the data.
Anti-Patterns An anti-pattern is published as a warning. It appears useful, but in practice is not effective. An anti-pattern usually is more than a mere bad habit, sloppy practice or half-baked idea: many are significant mistakes or unsolvable problems. By examining repetitive mistakes in trying unsuccessfully to apply an antipattern, we can learn to recognize the underlying problems and how to fix or escape them. Some patterns in this article might be anti-patterns for you—not useful, misleading or even dangerous. I have not sought to identify the anti-patterns because (1) in a context-dependent world, one person’s meat is another’s poison, and (2) our experience with these patterns is too new, and our understanding is not sufficiently mature to choose winners and losers yet. Ross Collard is founder of Collard & Company, a Manhattan-based consulting firm that specializes in software quality.
MAY 2009