ﻻ يوجد ملخص باللغة العربية
In the context of End-to-End testing of web applications, automated exploration techniques (a.k.a. crawling) are widely used to infer state-based models of the site under test. These models, in which states represent features of the web application and transitions represent reachability relationships, can be used for several model-based testing tasks, such as test case generation. However, current exploration techniques often lead to models containing many near-duplicate states, i.e., states representing slightly different pages that are in fact instances of the same feature. This has a negative impact on the subsequent model-based testing tasks, adversely affecting, for example, size, running time, and achieved coverage of generated test suites. As a web page can be naturally represented by its tree-structured DOM representation, we propose a novel near-duplicate detection technique to improve the model inference of web applications, based on Tree Kernel (TK) functions. TKs are a class of functions that compute similarity between tree-structured objects, largely investigated and successfully applied in the Natural Language Processing domain. To evaluate the capability of the proposed approach in detecting near-duplicate web pages, we conducted preliminary classification experiments on a freely-available massive dataset of about 100k manually annotated web page pairs. We compared the classification performance of the proposed approach with other state-of-the-art near-duplicate detection techniques. Preliminary results show that our approach performs better than state-of-the-art techniques in the near-duplicate detection classification task. These promising results show that TKs can be applied to near-duplicate detection in the context of web application model inference, and motivate further research in this direction.
Web testing has long been recognized as a notoriously difficult task. Even nowadays, web testing still heavily relies on manual efforts while automated web testing is far from achieving human-level performance. Key challenges in web testing include d
Model inference aims to extract accurate models from the execution logs of software systems. However, in reality, logs may contain some noise that could deteriorate the performance of model inference. One form of noise can commonly be found in system
Various software features such as classes, methods, requirements, and tests often have similar functionality. This can lead to emergence of duplicates in their descriptive documentation. Uncontrolled duplicates created via copy/paste hinder the proce
Diversity has been proposed as a key criterion to improve testing effectiveness and efficiency.It can be used to optimise large test repositories but also to visualise test maintenance issues and raise practitioners awareness about waste in test arte
Motivation: Automatically testing changes to code is an essential feature of continuous integration. For open-source code, without licensed dependencies, a variety of continuous integration services exist. The COnstraint-Based Reconstruction and An