Automated gui testing guided by usage profiles

Most software developed in recent years has a graphical user interface (GUI). The only way for the end-user to interact with the software application is through the GUI. Hence, acceptance and system testing of the software requires GUI testing. This paper presents a new technique for testing of GUI applications. Information on the actual usage of the application, in the form of “usage profiles, ” is used to ensure that a new version of the application will function correctly. Usage profiles, sequences of events that end-users execute on a GUI, are used to develop a probabilistic usage model of the application. An algorithm uses the model to generate test cases that represent events the user is most likely to execute. Reverse engineering methods are used to extract the underlying structure of the application. An em-pirical study on four open source GUI applications reveals that test suites generated from the probabilistic model are 0.2-22 % of the size of test suites produced directly from us-age profiles. Furthermore, the test suites generated from the model detect more faults per test case than those de-tected directly from the usage profiles, and detect faults not detected by the original profiles

Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18

Continuous, Evolutionary and Large-Scale: A New Perspective for Automated Mobile App Testing

Mobile app development involves a unique set of challenges including device fragmentation and rapidly evolving platforms, making testing a difficult task. The design space for a comprehensive mobile testing strategy includes features, inputs, potential contextual app states, and large combinations of devices and underlying platforms. Therefore, automated testing is an essential activity of the development process. However, current state of the art of automated testing tools for mobile apps poses limitations that has driven a preference for manual testing in practice. As of today, there is no comprehensive automated solution for mobile testing that overcomes fundamental issues such as automated oracles, history awareness in test cases, or automated evolution of test cases. In this perspective paper we survey the current state of the art in terms of the frameworks, tools, and services available to developers to aid in mobile testing, highlighting present shortcomings. Next, we provide commentary on current key challenges that restrict the possibility of a comprehensive, effective, and practical automated testing solution. Finally, we offer our vision of a comprehensive mobile app testing framework, complete with research agenda, that is succinctly summarized along three principles: Continuous, Evolutionary and Large-scale (CEL).Comment: 12 pages, accepted to the Proceedings of 33rd IEEE International Conference on Software Maintenance and Evolution (ICSME'17

Guiding Random Graphical and Natural User Interface Testing Through Domain Knowledge

Users have access to a diverse set of interfaces that can be used to interact with software. Tools exist for automatically generating test data for an application, but the data required by each user interface is complex. Generating realistic data similar to that of a user is difficult. The environment which an application is running inside may also limit the data available, or updates to an operating system can break support for tools that generate test data. Consequently, applications exist for which there are no automated methods of generating test data similar to that which a user would provide through real usage of a user interface. With no automated method of generating data, the cost of testing increases and there is an increased chance of bugs being released into production code. In this thesis, we investigate techniques which aim to mimic users, observing how stored user interactions can be split to generate data targeted at specific states of an application, or to generate different subareas of the data structure provided by a user interface. To reduce the cost of gathering and labelling graphical user interface data, we look at generating randomised screen shots of applications, which can be automatically labelled and used in the training stage of a machine learning model. These trained models could guide a randomised approach at generating tests, achieving a significantly higher branch coverage than an unguided random approach. However, for natural user interfaces, which allow interaction through body tracking, we could not learn such a model through generated data. We find that models derived from real user data can generate tests with a significantly higher branch coverage than a purely random tester for both natural and graphical user interfaces. Our approaches use no feedback from an application during test generation. Consequently, the models are “generating data in the dark”. Despite this, these models can still generate tests with a higher coverage than random testing, but there may be a benefit to inferring the current state of an application and using this to guide data generation

Performance Enhancement of Android Application Testing using Android Devices as a Service Cloud Model

The recent spurt in Android devices has given rise to the development of millions of Android applications. Hence there is a need for rapid and efficient testing techniques to satiate the global app demand. Hence the paper proposes an automated testing framework by deployment testing practices on the cloud. Android devices could be tested by connecting them through wired or wireless connections. This framework can be addressed as Android Testing as a Service (ATaaS) which assists in carrying out functional testing, performance testing etc. The paper also demonstrates the use of a MAT tool to carryout automated testing, the results of which can be compared to conventional testing practices. It also gives us a gist of virtualization. Hence Cloud Testing Platform (CTP) aims at providing all the conventional testing practices at higher speed, availability and lower cost of testing these applications and the limited resources available in mobile devices.. Developers can conduct compatibility tests in various Android devices only by uploading test programs and scripts to cloud testing platforms

Defining and Evaluating Test Suite Consolidation for Event Sequence-based Test Cases

This research presents a new test suite consolidation technique, called CONTEST, for automated GUI testing. A new probabilistic model of the GUI is developed to allow direct application of CONTEST. Multiple existing test suites are used to populate the model and compute probabilities based on the observed event sequences. These probabilities are used to generate a new test suite that consolidates the original ones. A new test suite similarity metric, called CONTeSSi(n), is introduced which compares multiple event sequence-based test suites using relative event positions. Results of empirical studies showed that CONTEST yields a test suite that achieves better fault detection and code coverage than the original suites, and that the CONTeSSi(n) metric is a better indicator of the similarity between sequence-based test suites than existing metrics

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System