Modern business applications predominantly rely on web technology, enabling software vendors to efficiently provide them as a service, removing some of the complexity of the traditional release and update process. While this facilitates shorter, more efficient and frequent release cycles, it requires continuous testing. Having insight into application behavior through explicit models can largely support development, testing and maintenance. Model-based testing allows efficient test creation based on a description of the states the application can be in and the transitions between these states. As specifying behavior models that are precise enough to be executable by a test automation tool is a hard task, an alternative is to extract them from running applications. However, mining such models is a challenge, in particular because one needs to know when two states are equivalent, as well as how to reach that state. We present Process Crawler (ProCrawl), a tool to mine behavior models from web applications that support multi-user workflows. ProCrawl incrementally learns a model by generating program runs and observing the application behavior through the user interface. In our evaluation on several real-world web applications, ProCrawl extracted models that concisely describe the implemented workflows and can be directly used for model-based testing.

Today's web applications demand very high release cycles--and consequently, frequent tests. Automating these tests typically requires a behavior model: A description of the states the application can be in, the transitions between these states, and the expected results. Furthermore one needs scripts to make the abstract actions (transitions) in the model executable. However, specifying such behavior models and writing the necessary scripts manually is a hard task. We present ProCrawl (Process Crawler), a tool that automatically mines (extended) finite-state machines from (multi-user) web applications and generates executable test scripts. ProCrawl explores the behavior of the application by systematically generating program runs and observing changes on the application's user interface. The resulting models can be directly used for effective model-based testing, in particular regression testing.

Today's enterprise web applications demand very high release cycles---and consequently, frequent tests. Automating these tests typically requires a behavior model: A description of the states the application can be in, the transitions between these states, and the expected results. Furthermore one needs scripts to make the abstract actions (transitions) in the model executable. As specifying such behavior models and writing the necessary scripts manually is a hard task, a possible alternative could be to extract them from existing applications. However, mining such models can be a challenge, in particular because one needs to know when two states are equivalent, as well as how to reach that state. We present ProCrawl (PROcess CRAWLer), a generic approach to mine behavior models from (multi-user) enterprise web applications. ProCrawl observes the behavior of the application through its user interface, generates and executes tests to explore unobserved behavior. In our evaluation of three non-trivial web applications (an open-source shop system, an SAP product compliance application, and an open-source conference manager), ProCrawl produces models that precisely abstract application behavior and which can be directly used for effective model-based regression testing.

Specification mining infers abstractions over a set of program execution traces. Whereas inductive approaches to specification mining rely on a given set of execution traces, experimental approaches systematically generate and execute test cases to infer rich models including uncommon and exceptional behavior. State-of-the-art experimental mining approaches infer low-level models representing the behavior of single classes. This paper proposes an approach for inferring models of built-in processes in enterprise systems based on systematic scenario test generation. The paper motivates the approach, sketches the relevant concepts and challenges, and discusses related work.