ChimpCheck: Property-Based Randomized Test Generation for Interactive Apps

We consider the problem of generating relevant execution traces to test rich interactive applications. Rich interactive applications, such as apps on mobile platforms, are complex stateful and often distributed systems where sufficiently exercising the app with user-interaction (UI) event sequences to expose defects is both hard and time-consuming. In particular, there is a fundamental tension between brute-force random UI exercising tools, which are fully-automated but offer low relevance, and UI test scripts, which are manual but offer high relevance. In this paper, we consider a middle way---enabling a seamless fusion of scripted and randomized UI testing. This fusion is prototyped in a testing tool called ChimpCheck for programming, generating, and executing property-based randomized test cases for Android apps. Our approach realizes this fusion by offering a high-level, embedded domain-specific language for defining custom generators of simulated user-interaction event sequences. What follows is a combinator library built on industrial strength frameworks for property-based testing (ScalaCheck) and Android testing (Android JUnit and Espresso) to implement property-based randomized testing for Android development. Driven by real, reported issues in open source Android apps, we show, through case studies, how ChimpCheck enables expressing effective testing patterns in a compact manner.Comment: 20 pages, 21 figures, Symposium on New ideas, New Paradigms, and Reflections on Programming and Software (Onward!2017

LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunities

This paper investigates the application of large language models (LLM) in the domain of mobile application test script generation. Test script generation is a vital component of software testing, enabling efficient and reliable automation of repetitive test tasks. However, existing generation approaches often encounter limitations, such as difficulties in accurately capturing and reproducing test scripts across diverse devices, platforms, and applications. These challenges arise due to differences in screen sizes, input modalities, platform behaviors, API inconsistencies, and application architectures. Overcoming these limitations is crucial for achieving robust and comprehensive test automation. By leveraging the capabilities of LLMs, we aim to address these challenges and explore its potential as a versatile tool for test automation. We investigate how well LLMs can adapt to diverse devices and systems while accurately capturing and generating test scripts. Additionally, we evaluate its cross-platform generation capabilities by assessing its ability to handle operating system variations and platform-specific behaviors. Furthermore, we explore the application of LLMs in cross-app migration, where it generates test scripts across different applications and software environments based on existing scripts. Throughout the investigation, we analyze its adaptability to various user interfaces, app architectures, and interaction patterns, ensuring accurate script generation and compatibility. The findings of this research contribute to the understanding of LLMs' capabilities in test automation. Ultimately, this research aims to enhance software testing practices, empowering app developers to achieve higher levels of software quality and development efficiency.Comment: Accepted by the 23rd IEEE International Conference on Software Quality, Reliability, and Security (QRS 2023

Translating Video Recordings of Mobile App Usages into Replayable Scenarios

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers. Unfortunately, automatically analyzing screen recordings presents serious challenges, due to their graphical nature, compared to other types of (textual) artifacts. To address these challenges, this paper introduces V2S, a lightweight, automated approach for translating video recordings of Android app usages into replayable scenarios. V2S is based primarily on computer vision techniques and adapts recent solutions for object detection and image classification to detect and classify user actions captured in a video, and convert these into a replayable test scenario. We performed an extensive evaluation of V2S involving 175 videos depicting 3,534 GUI-based actions collected from users exercising features and reproducing bugs from over 80 popular Android apps. Our results illustrate that V2S can accurately replay scenarios from screen recordings, and is capable of reproducing $\approx$ 89% of our collected videos with minimal overhead. A case study with three industrial partners illustrates the potential usefulness of V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software Engineering (ICSE'20), 13 page

Mobile application testing matrix and challenges

The adoption of smart phones and the usages of mobile applications are increasing rapidly. Consequently, within limited time-range, mobile Internet usages have managed to take over the desktop usages particularly since the first smart phone-touched application released by iPhone in 2007. This paper is proposed to provide solution and answer the most demandable questions related to mobile application automated and manual testing limitations. Moreover, Mobile application testing requires agility and physically testing. Agile testing is to detect bugs through automated tools, whereas the compatibility testing is more to ensure that the apps operates on mobile OS (Operation Systems) as well as on the different real devices. Moreover, we have managed to answer automated or manual questions through two mobile application case studies MES (Mobile Exam System) and MLM (Mobile Lab Mate) by creating test scripts for both case studies and our experiment results have been discussed and evaluated on whether to adopt test on real devices or on emulators? In addition to this, we have introduced new mobile application testing matrix for the testers and some enterprises to obtain knowledge fro

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

Towards Automated Translation between Generations of GUI-based Tests for Mobile Devices

Market demands for faster delivery and higher software quality are progressively becoming more stringent. A key hindrance for software companies to meet such demands is how to test the software due to to the intrinsic costs of development, maintenance and evolution of testware. Especially since testware should be defined, and aligned, with all layers of system under test (SUT), including all graphical user interface (GUI) abstraction levels. These levels can be tested with different generations of GUI-based test approaches, where 2nd generation, or Layout-based, tests leverage GUI properties and 3rd generation, or Visual, tests make use of image recognition. The two approaches provide different benefits and drawbacks and are seldom used together because of the aforementioned costs, despite growing academic evidence of the complementary benefits. In this work we propose the proof of concept of a novel two-step translation approach for Android GUI testing, where a translator first creates a technology independent script with actions and elements of the GUI, and then translates it to a script with the syntax chosen by the user. The approach enables users to translate Layout-based to Visual scripts and vice versa, to gain the benefits (e.g. robustness, speed and ability to emulate the user) of both generations, whilst minimizing the drawbacks (e.g. development and maintenance costs). We outline our approach from a technical perspective, discuss some of the key challenges with the realization of our approach, evaluate the feasibility and the advantages provided by our approach on an open-source Android application, and discuss the potential industrial impact of this work

A Quantitative Study of Java Software Buildability

Researchers, students and practitioners often encounter a situation when the build process of a third-party software system fails. In this paper, we aim to confirm this observation present mainly as anecdotal evidence so far. Using a virtual environment simulating a programmer's one, we try to fully automatically build target archives from the source code of over 7,200 open source Java projects. We found that more than 38% of builds ended in failure. Build log analysis reveals the largest portion of errors are dependency-related. We also conduct an association study of factors affecting build success

Evolution and Fragility of Mobile Automated Test Suites

L'abstract è presente nell'allegato / the abstract is in the attachmen