arXiv subjects

Shengcheng Yu

Publications and source records attributed to Shengcheng Yu.

24 records · Page 2Linked to original sources

arXiv2023

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.

cs.SE↗

arXiv2023

Mobile App Crowdsourced Test Report Consistency Detection via Deep Image-and-Text Fusion Understanding

Crowdsourced testing, as a distinct testing paradigm, has attracted much attention in software testing, especially in mobile application (app) testing field. Compared with in-house testing, crowdsourced testing shows superiority with the diverse testing environments when faced with the mobile testing fragmentation problem. However, crowdsourced testing also encounters the low-quality test report problem caused by unprofessional crowdworkers involved with different expertise. In order to handle the submitted reports of uneven quality, app developers have to distinguish high-quality reports from low-quality ones to help the bug inspection. One kind of typical low-quality test report is inconsistent test reports, which means the textual descriptions are not focusing on the attached bug-occurring screenshots. According to our empirical survey, only 18.07% crowdsourced test reports are consistent. Inconsistent reports cause waste on mobile app testing. To solve the inconsistency problem, we propose ReCoDe to detect the consistency of crowdsourced test reports via deep image-and-text fusion understanding. ReCoDe is a two-stage approach that first classifies the reports based on textual descriptions into different categories according to the bug feature. In the second stage, ReCoDe has a deep understanding of the GUI image features of the app screenshots and then applies different strategies to handle different types of bugs to detect the consistency of the crowdsourced test reports. We conduct an experiment on a dataset with over 22k test reports to evaluate ReCoDe, and the results show the effectiveness of ReCoDe in detecting the consistency of crowdsourced test reports. Besides, a user study is conducted to prove the practical value of ReCoDe in effectively helping app developers improve the efficiency of reviewing the crowdsourced test reports.

cs.SE↗

arXiv2022

Test Case Prioritization Using Partial Attention

Test case prioritization (TCP) aims to reorder the regression test suite with a goal of increasing the fault detection rate. Various TCP techniques have been proposed based on different prioritization strategies. Among them, the greedy-based techniques are the most widely-used TCP techniques. However, existing greedy-based techniques usually reorder all candidate test cases in prioritization iterations, resulting in both efficiency and effectiveness problems. In this paper, we propose a generic partial attention mechanism, which adopts the previous priority values (i.e., the number of additionally-covered code units) to avoid considering all candidate test cases. Incorporating the mechanism with the additional-greedy strategy, we implement a novel coverage-based TCP technique based on partition ordering (OCP). OCP first groups the candidate test cases into different partitions and updates the partitions on the descending order. We conduct a comprehensive experiment on 19 versions of Java programs and 30 versions of C programs to compare the effectiveness and efficiency of OCP with six state-of-the-art TCP techniques: total-greedy, additional-greedy, lexicographical-greedy, unify-greedy, art-based, and search-based. The experimental results show that OCP achieves a better fault detection rate than the state-of-the-arts. Moreover, the time costs of OCP are found to achieve 85%-99% improvement than most state-of-the-arts.

cs.SE↗

arXiv2021

Layout and Image Recognition Driving Cross-Platform Automated Mobile Testing

The fragmentation problem has extended from Android to different platforms, such as iOS, mobile web, and even mini-programs within some applications (app). In such a situation, recording and replaying test scripts is a popular automated mobile app testing approaches. But such approach encounters severe problems when crossing platforms. Different versions of the same app need to be developed to support different platforms relying on different platform supports. Therefore, mobile app developers need to develop and maintain test scripts for multiple platforms aimed at completely the same test requirements, greatly increasing testing costs. However, we discover that developers adopt highly similar user interface layouts for versions of the same app on different platforms. Such a phenomenon inspires us to replay test scripts from the perspective of similar UI layouts. We propose an image-driven mobile app testing framework, utilizing Widget Feature Matching and Layout Characterization Matching. We use computer vision technologies to perform UI feature comparison and layout hierarchy extraction on app screenshots to obtain UI structures with rich contextual information, including coordinates, relative relationship, etc. Based on acquired UI structures, we can form a platform-independent test script, and then locate the target widgets under test. Thus, the proposed framework non-intrusively replays test scripts according to a novel platform-independent test script model. We also design and implement a tool named LIT to devote the proposed framework into practice, based on which, we conduct an empirical study to evaluate the effectiveness and usability of the proposed testing framework. Results show that the overall replay accuracy reaches around 63.39% on Android (14% improvement over state-of-the-art approaches) and 21.83% on iOS (98% improvement over state-of-the-art approaches).

cs.SE↗

arXiv2021

Prioritize Crowdsourced Test Reports via Deep Screenshot Understanding

Crowdsourced testing is increasingly dominant in mobile application (app) testing, but it is a great burden for app developers to inspect the incredible number of test reports. Many researches have been proposed to deal with test reports based only on texts or additionally simple image features. However, in mobile app testing, texts contained in test reports are condensed and the information is inadequate. Many screenshots are included as complements that contain much richer information beyond texts. This trend motivates us to prioritize crowdsourced test reports based on a deep screenshot understanding. In this paper, we present a novel crowdsourced test report prioritization approach, namely DeepPrior. We first represent the crowdsourced test reports with a novelly introduced feature, namely DeepFeature, that includes all the widgets along with their texts, coordinates, types, and even intents based on the deep analysis of the app screenshots, and the textual descriptions in the crowdsourced test reports. DeepFeature includes the Bug Feature, which directly describes the bugs, and the Context Feature, which depicts the thorough context of the bug. The similarity of the DeepFeature is used to represent the test reports' similarity and prioritize the crowdsourced test reports. We formally define the similarity as DeepSimilarity. We also conduct an empirical experiment to evaluate the effectiveness of the proposed technique with a large dataset group. The results show that DeepPrior is promising, and it outperforms the state-of-the-art approach with less than half the overhead.

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arXiv2019

From Data Quality to Model Quality: an Exploratory Study on Deep Learning

Nowadays, people strive to improve the accuracy of deep learning models. However, very little work has focused on the quality of data sets. In fact, data quality determines model quality. Therefore, it is important for us to make research on how data quality affects on model quality. In this paper, we mainly consider four aspects of data quality, including Dataset Equilibrium, Dataset Size, Quality of Label, Dataset Contamination. We deign experiment on MNIST and Cifar-10 and try to find out the influence the four aspects make on model quality. Experimental results show that four aspects all have decisive impact on the quality of models. It means that decrease in data quality in these aspects will reduce the accuracy of model.

cs.LG↗