Mining Workflow Models from Web Applications

Abstract

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 ProCrawl (Process Crawler), 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.

Guess the State: Exploiting Determinism to Improve GUI Exploration Efficiency

“…Choosing appropriate abstraction functions can have a great impact on the performances of SIBILLA, with the optimal choice being context dependent in most cases, as also noted in other papers that studied the impact of state abstractions [3], [12], [19]. For this reason, our current prototype of SIBILLA is equipped with multiple abstraction functions, which can be selected as configuration parameters, and is designed to be easily extensible to work with further abstraction functions that could be defined in the future for different use cases.…”

Drowsiness Detection System

“…Another approach is to make use of Raspberry Pi 3.This is done by using a Raspberry Pi 3 module and camera using input from 10 volunteers.Flash JavaScript(FlashJaX) can also be made use of by implementing a cross platform.This can be achieved by using JavaScript and AdobeActionScript.A browser that supports cloning of the document node is created by FlashJaX and its given as input.Through this,Web security and protection from untrusted sites can be achieved.Mining behavior models from web applications using Process Crawler(ProCrawl) is used [10].A configured set of UI views are given as input for this case.This helps bring development, testing and maintenance to an easier level by removing any kind of complexity.Discovering vulnerabilities in the source code with fewer false positives [6] has also been successfully achieved using WAP. A large set of PHP applications are used for the input .This is done in order to reduce vulnerability in source codes written in unsafe languages.Facial expression recognition using RGB, 3D, Thermal and multi-modal approach has also been implemented successfully.…”