A Comparative Study of Automated Software Testing Tools

Software testing is an integral phase in Software Development Life Cycle (SDLC) process. Testing assesses the functionalities of a software item and quality of the product. Automated software testing utilizes different tools to execute testing activities. In this paper, I have discussed the features of automated and manual testing as well as analyzed three automated software testing tools: Selenium, UFT/QTP and Watir. In brief, I have presented a detailed description focusing on multiple feature set, efficiency, simplicity and usability of each tool. I also evaluated, tested and compared the different aspects of Selenium, UFT/QTP and Watir. Finally, this research allowed me to draw some solid differences between automated and manual testing as well as learn and explore various characteristics of automated testing tools by having real-world experience of testing effectively

Guiding ChatGPT to Fix Web UI Tests via Explanation-Consistency Checking

The rapid evolution of Web UI incurs time and effort in maintaining UI tests. Existing techniques in Web UI test repair focus on finding the target elements on the new web page that match the old ones so that the corresponding broken statements can be repaired. We present the first study that investigates the feasibility of using prior Web UI repair techniques for initial local matching and then using ChatGPT to perform global matching. Our key insight is that given a list of elements matched by prior techniques, ChatGPT can leverage the language understanding to perform global view matching and use its code generation model for fixing the broken statements. To mitigate hallucination in ChatGPT, we design an explanation validator that checks whether the provided explanation for the matching results is consistent, and provides hints to ChatGPT via a self-correction prompt to further improve its results. Our evaluation on a widely used dataset shows that the ChatGPT-enhanced techniques improve the effectiveness of existing Web test repair techniques. Our study also shares several important insights in improving future Web UI test repair techniques

A formal approach to automatically analyse extra-functionalproperties in mobile applications.

This paper presents an integrated approach for testing mobile applications (apps) against a set of extra-functional properties to be used by app developers. The approach starts with the (manual or automatic)extraction of the interaction model, that is, a formal model of the potential user interactions with the app.The model is constructed to allow a model checking tool to exhaustively extract the so-called app user flows, that is, the sequences of user actions, that constitute the test cases. In the final step, the app user flows are executed on the app running on real devices. The resulting execution traces are enriched with different measures and verified against a set of extra-functional properties of interest. The approach has been adapted to analyse several applications running at the same time with several devices supporting the applications.This paper presents the definition and formalization of both the modelling language for the interaction model and the specification language to represent the extra-functional properties. It also describes a methodology for automatically extracting the model. Finally, it presents an implementation focused on Android apps, which is integrated in the TRIANGLE testing framework, and the evaluation of the approach.Work is partially supported by the Spanish Ministry of Economy and Competitiveness projectTIN2015-67083-R. This project has received funding from the European Union’s Horizon 2020research and innovation programme under grant agreement no. 688712 (TRIANGLE project)

Alustariippumattomien mobiilisovellusten testauksen automatisoiminen

Mobile applications are becoming more common as the number of mobile devices grows. For these devices there are a number of operating systems that run applications that have been made for them. Implementing an application for multiple platforms has commonly required creating multiple implementations in order to run the application on each of the desired platforms. This has lead to the development of cross-platform mobile applications, which allow writing one implementation that can be used for multiple platforms. In this thesis, the intent is to evaluate if there are tools for automating testing cross-platform mobile applications, that are viable for using for testing mobile applications developed by Dicode Ltd. The tool used for developing cross-platform mobile applications is PhoneGap. This thesis evaluates three available tools for testing cross-platform mobile applications. The target platforms in this evaluation are Android and iOS. A set of criteria are used to evaluate the frameworks. The results of this thesis recommend the use of a framework called Calabash for automating the testing of cross-platform mobile applications. Calabash performed well with all of the evaluation criteria and it is able to test Android and iOS applications. These are the two most popular operating systems for smartphones

GUI Element Identification with Semantic Mapping

User Interface test automation faces significant obstacles due to test failures connected to application changes. Additionally, current User Interface testing methods are not context aware and usage-based, which makes exploring web application functionality challenging. Robots used for crawling web application interfaces are slow and do not reflect human interaction with them. Semantic mapping (semantic matching) has been proposed as a method for reusing existing tests between web applications in the same domain to mitigate issues with testing speed and context awareness. This thesis explores semantic mapping for robust User Interface element identification that could alleviate the issue with test failures upon application changes. Semantic mapping uses textual cues of User Interface elements neighboring testable features to identify similar features in other applications of the same domain. This work argues that the same technique can be applied to various versions of the same web application. Existing tools leverage text attributes of features' neighbors based on the hierarchy and position of an element, while this study applies semi-supervised learning methods to extract relevant text from elements surrounding features. It uses state-of-the-art pre-trained language models for embedding textual cues. To find similar features, it uses cosine similarity between sentences as a measure of semantic similarity. This implementation of semantic matching has demonstrated promising results for User Interface element identification between two versions of the same web application