Paper Info
Title
ALEX: Mixed-Mode Learning of Web Applications at Ease.
Abstract
In this paper, we present ALEX, a web application that enables non-programmers to fully automatically infer models of web applications via active automata learning. It guides the user in setting up dedicated learning scenarios, and invites her to experiment with the available options in order to infer models at adequate levels of abstraction. In the course of this process, characteristics that go beyond a mere "site map" can be revealed, such as hidden states that are often either specifically designed or indicate errors in the application logic. Characteristic for ALEX is its support for mixed-mode learning: REST and web services can be executed simultaneously in one learning experiment, which is ideal when trying to compare back-end and front-end functionality of a web application. ALEX has been evaluated in a comparative study with 140 undergraduate students, which impressively highlighted its potential to make formal methods like active automata learning more accessible to a non-expert crowd.
Year
DOI
Venue
2016
10.1007/978-3-319-47169-3_51
Lecture Notes in Computer Science
Keywords
Field
DocType
Active automata learning,Mixed-mode learning,Specification mining,Web services,Web applications
Site map,World Wide Web,Abstraction,Computer science,Mixed mode,Application logic,Formal methods,Web application,Web service,Automata learning
Conference
Volume
ISSN
Citations 
9953
0302-9743
1
PageRank 
References 
Authors
0.34
19
6
Name
Order
Citations
PageRank
Alexander Bainczyk110.68
Alexander Schieweck210.34
Malte Isberner315814.09
Tiziana Margaria42098247.17
Johannes Neubauer5798.78
Bernhard Steffen64239423.70