API Testing Method with Markov Models

Summary:

This paper presents a technique to test APIs that combines aspects of two published software testing methods, namely Markov modeling and category partitioning. Markov modeling provides a basis for model based testing and establishes the context for generating API calls and call sequences within a single test case.

For modeling purposes, each combination of parameter values for each function call is a unique "input." Category partitioning helps select parameter values and effective combinations of multiple parameters for individual API calls.

Small examples demonstrate these techniques and two case study summaries illustrateits effectiveness. One case under laboratory conditions established proof-of-concept and the other applicability to a large commercial API. Some aspects of these techniques are manually intensive and suggest a need for automation.

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About the author

James Whittaker

James A. Whittaker is is a technology executive with a career that spans academia, start-ups, and industry. He was an early thought leader in model-based testing where his Ph.D. dissertation became a standard reference on the subject. While a professor at the Florida Institute of Technology, James founded the world's largest academic software testing research center and helped make testing a degree track for undergraduates. He wrote How to Break Software, How to Break Software Security (with Hugh Thompson), and How to Break Web Software (with Mike Andrews). While at Microsoft, James transformed many of his testing ideas into tools and techniques for developers and testers, and wrote the book Exploratory Software Testing. For the past three years he worked for Google as an engineering director where he co-wrote How Google Tests Software (with Jason Arbon and Jeff Carollo). He's currently a development manager at Microsoft where he is busy re-inventing the web.