اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدVisual Testing of GUIs by Abstraction
97
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Ensuring the correct visual appearance of graphical user interfaces (GUIs) is important because visual bugs can cause substantial losses for businesses. An application might behave functionally correct in an automated test, but visual bugs can make the GUI effectively unusable for the user. Most of todays approaches for visual testing are pixel-based and tend to have flaws that are characteristic for image differencing. For instance, minor and unimportant visual changes often cause false positives, which confuse the user with unnecessary error reports. Our idea is to introduce an abstract GUI state (AGS), where we define structural relations to identify relevant GUI changes and ignore those that are unimportant from the users point of view. In addition, we explore several strategies to address the GUI element identification problem in terms of AGS. This allows us to provide rich diagnostic information that help the user to better interpret changes. Based on the principles of golden master testing, we can support a fully-automated approach to visual testing by using the AGS. We have implemented our approach to visually test web pages and our experiments show that we are able to reliably detect GUI changes.
قيم البحث
اقرأ أيضاً
In this paper, our aim is to propose a model for code abstraction, based on abstract interpretation, allowing us to improve the precision of a recently proposed static analysis by abstract interpretation of dynamic languages. The problem we tackle he
re is that the analysis may add some spurious code to the string-to-execute abstract value and this code may need some abstract representations in order to make it analyzable. This is precisely what we propose here, where we drive the code abstraction by the analysis we have to perform.
Context: Visual GUI testing (VGT) is referred to as the latest generation GUI-based testing. It is a tool-driven technique, which uses image recognition for interacting with and asserting the behavior of the system under test. Motivated by the indust
rial need of a large Turkish software and systems company providing solutions in the areas of defense and IT sector, an action-research project was recently initiated to implement VGT in several teams and projects in the company. Objective: To address the above needs, we planned and carried out an empirical investigation with the goal of assessing VGT using two tools (Sikuli and JAutomate). The purpose was to determine a suitable approach and tool for VGT of a given project (software product) in the company, increase the know-how in the companys test teams. Method: Using an action-research case-study design, we investigated the use of VGT in the studied organization. Specifically, using the two selected VGT tools, we conducted a quantitative and a qualitative evaluation of VGT. Results: By assessing the list of Challenges, Problems and Limitations (CPL), proposed in previous work, in the context of our empirical study, we found that test-tool- and SUT-related CPLs were quite comparable to a previous empirical study, e.g., the synchronization between SUT and test tools were not always robust and there were failures in test tools image recognition features. When assessing the types of test maintenance activities, when executing the automated test cases on ne
Testing is an important activity in engineering of industrial software. For such software, testing is usually performed manually by handcrafting test suites based on specific design techniques and domain-specific experience. To support developers in
testing, different approaches for producing good test suites have been proposed. In the last couple of years combinatorial testing has been explored with the goal of automatically combining the input values of the software based on a certain strategy. Pairwise testing is a combinatorial technique used to generate test suites by varying the values of each pair of input parameters to a system until all possible combinations of those parameters are created. There is some evidence suggesting that these kinds of techniques are efficient and relatively good at detecting software faults. Unfortunately, there is little experimental evidence on the comparison of these combinatorial testing techniques with, what is perceived as, rigorous manually handcrafted testing. In this study we compare pairwise test suites with test suites created manually by engineers for 45 industrial programs. The test suites were evaluated in terms of fault detection, code coverage and number of tests. The results of this study show that pairwise testing, while useful for achieving high code coverage and fault detection for the majority of the programs, is almost as effective in terms of fault detection as manual testing. The results also suggest that pairwise testing is just as good as manual testing at fault detection for 64% of the programs.
In previous work with Pous, we defined a semantics for CCS which may both be viewed as an innocent presheaf semantics and as a concurrent game semantics. It is here proved that a behavioural equivalence induced by this semantics on CCS processes is f
ully abstract for fair testing equivalence. The proof relies on a new algebraic notion called playground, which represents the rule of the game. From any playground, two languages, equipped with labelled transition systems, are derived, as well as a strong, functional bisimulation between them.
We distinguish two general modes of testing for Deep Neural Networks (DNNs): Offline testing where DNNs are tested as individual units based on test datasets obtained without involving the DNNs under test, and online testing where DNNs are embedded i
nto a specific application environment and tested in a closed-loop mode in interaction with the application environment. Typically, DNNs are subjected to both types of testing during their development life cycle where offline testing is applied immediately after DNN training and online testing follows after offline testing and once a DNN is deployed within a specific application environment. In this paper, we study the relationship between offline and online testing. Our goal is to determine how offline testing and online testing differ or complement one another and if offline testing results can be used to help reduce the cost of online testing? Though these questions are generally relevant to all autonomous systems, we study them in the context of automated driving systems where, as study subjects, we use DNNs automating end-to-end controls of steering functions of self-driving vehicles. Our results show that offline testing is less effective than online testing as many safety violations identified by online testing could not be identified by offline testing, while large prediction errors generated by offline testing always led to severe safety violations detectable by online testing. Further, we cannot exploit offline testing results to reduce the cost of online testing in practice since we are not able to identify specific situations where offline testing could be as accurate as online testing in identifying safety requirement violations.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
