Practical, Automated Scenario-based Mobile App Testing

• URL: http://arxiv.org/abs/2406.08340v1
• Date: Wed, 12 Jun 2024 15:48:39 GMT
• Title: Practical, Automated Scenario-based Mobile App Testing
• Authors: Shengcheng Yu, Chunrong Fang, Mingzhe Du, Zimin Ding, Zhenyu Chen, Zhendong Su,
• Abstract summary: Test scripts developed by human testers consider business logic by focusing on testing scenarios. Due to the GUI-intensive feature of mobile apps, human testers always understand app GUI to organize test scripts for scenarios. ScenTest tries to start automated testing by imitating human practices and integrating domain knowledge into scenario-based mobile app testing.
• Score: 13.52057950260007
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The importance of mobile application (app) quality insurance is increasing with the rapid development of the mobile Internet. Automated test generation approaches, as a dominant direction of app quality insurance, follow specific models or strategies, targeting at optimizing the code coverage. Such approaches lead to a huge gap between testing execution and app business logic. Test scripts developed by human testers consider business logic by focusing on testing scenarios. Due to the GUI-intensive feature of mobile apps, human testers always understand app GUI to organize test scripts for scenarios. This inspires us to utilize domain knowledge from app GUI understanding for scenario-based test generation. In this paper, we propose a novel approach, ScenTest, for scenario-based mobile app testing with event knowledge graph (EKG) via GUI image understanding. ScenTest tries to start automated testing by imitating human practices and integrating domain knowledge into scenario-based mobile app testing, realizing fully automated testing on target testing scenarios for the first time. ScenTest extracts four kinds of entities and five kinds of corresponding relationships from crowdsourced test reports, where the test events and app GUI information are presented, and constructs the EKGs for specific scenarios. Then, ScenTest conducts test generation for specific scenarios on different apps with the guidance of EKG with the combination consideration of app current state and testing context. We conduct an evaluation on ScenTest on different aspects. The results show that the test generation of ScenTest on the basis of EKG is effective, and ScenTest can reveal 80+ distinct real-world bugs in specific scenarios compared with representative baselines.

Related papers

• AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
  We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs. Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
  arXiv  Detail & Related papers  (2024-11-02T13:24:30Z)
• Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models [49.06068319380296]
  We introduce context-aware testing (CAT) which uses context as an inductive bias to guide the search for meaningful model failures. We instantiate the first CAT system, SMART Testing, which employs large language models to hypothesize relevant and likely failures.
  arXiv  Detail & Related papers  (2024-10-31T15:06:16Z)
• Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests [4.574205608859157]
  We introduce UTGen, which combines search-based software testing and large language models to enhance the understandability of automatically generated test cases. We observe that participants working on assignments with UTGen test cases fix up to 33% more bugs and use up to 20% less time when compared to baseline test cases.
  arXiv  Detail & Related papers  (2024-08-21T15:35:34Z)
• A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites [1.4563527353943984]
  Large Language Models (LLMs) have been applied to various aspects of software development. We present AgoneTest: an automated system for generating test suites for Java projects.
  arXiv  Detail & Related papers  (2024-08-14T23:02:16Z)
• KAT: Dependency-aware Automated API Testing with Large Language Models [1.7264233311359707]
  KAT (Katalon API Testing) is a novel AI-driven approach that autonomously generates test cases to validate APIs. Our evaluation of KAT using 12 real-world services shows that it can improve validation coverage, detect more undocumented status codes, and reduce false positives in these services.
  arXiv  Detail & Related papers  (2024-07-14T14:48:18Z)
• Generating Test Scenarios from NL Requirements using Retrieval-Augmented LLMs: An Industrial Study [5.179738379203527]
  This paper presents an automated approach (RAGTAG) for test scenario generation using Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) We evaluate RAGTAG on two industrial projects from Austrian Post with bilingual requirements in German and English.
  arXiv  Detail & Related papers  (2024-04-19T10:27:40Z)
• Observation-based unit test generation at Meta [52.4716552057909]
  TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution. TestGen has landed 518 tests into production, which have been executed 9,617,349 times in continuous integration, finding 5,702 faults. Our evaluation reveals that, when carving its observations from 4,361 reliable end-to-end tests, TestGen was able to generate tests for at least 86% of the classes covered by end-to-end tests.
  arXiv  Detail & Related papers  (2024-02-09T00:34:39Z)
• Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems [80.77917437785773]
  Task-oriented dialogue systems ( TDSs) are assessed mainly in an offline setting or through human evaluation. We propose a metaphorical user simulator for end-to-end TDS evaluation, where we define a simulator to be metaphorical if it simulates user's analogical thinking in interactions with systems. We also propose a tester-based evaluation framework to generate variants, i.e., dialogue systems with different capabilities.
  arXiv  Detail & Related papers  (2022-04-02T05:11:03Z)
• Nirikshak: A Clustering Based Autonomous API Testing Framework [0.0]
  Nirikshak is a self-reliant testing framework for REST API testing. It achieves level 2 of autonomy in executing REST API testing procedures. Nirikshak is publicly available as an open-source software for the community at https://github.com/yashmahalwal/nirikshak.
  arXiv  Detail & Related papers  (2021-12-15T18:05:27Z)
• Realistic simulation of users for IT systems in cyber ranges [63.20765930558542]
  We instrument each machine by means of an external agent to generate user activity. This agent combines both deterministic and deep learning based methods to adapt to different environment. We also propose conditional text generation models to facilitate the creation of conversations and documents.
  arXiv  Detail & Related papers  (2021-11-23T10:53:29Z)
• Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework [68.96770035057716]
  A/B testing is a business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. This paper introduces a reinforcement learning framework for carrying A/B testing in online experiments.
  arXiv  Detail & Related papers  (2020-02-05T10:25:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.