[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82583-en":3,"doc-seo-82583-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82583,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","A Model-based Testing Technique for Amazon Lex Task-based Chatbots","Task-based chatbots are widely deployed to help users complete real-world tasks through conversational interfaces, yet they remain vulnerable to software bugs. Existing quality assessment methods often generate oversimplified test scenarios and suffer from oracle weaknesses when deciding whether a conversation achieved the intended task. LexTester is presented as an automated model-based testing technique for Amazon Lex chatbots. It builds a Dialog Graph of possible interactions and derives executable tests via coverage strategies, evaluated against Botium on five bots with higher coverage and up to fourfold fault-detection gains.","arXiv :2607 .0 1094v 1 [ cs . SE] 1 Jul 2026  \nA Model-based Testing Technique for Amazon Lex Task-based Chatbots  \nDiego Clerissi⋆, Alessandro Vasina, and Leonardo Mariani  \nDepartment of Informatics, Systems and Communication University of Milano-Bicocca  \nMilan, Italy  \n{[firstname}.{lastname}@unimib.it](firstname}.{lastname}@unimib.it)  \nAbstract. Task-based chatbots are nowadays widely adopted software systems, usually integrated into real-world applications and communication channels, designed to assist users in completing tasks through conversational interfaces. Like any other software, even chatbots are prone to bugs. Despite their increasing pervasiveness in everyday activities, existing techniques for assessing their quality still exhibit several limitations, such as the simplicity of generated test scenarios and oracle weaknesses.  \nIn this paper, we present LexTester, an automated model-based testing technique for Amazon Lex chatbots. The technique explores the conversational space of the chatbot under test to generate a Dialog Graph of all possible interactions, from which an executable test suite is generated according to different coverage strategies. LexTester was evaluated against the state-of-the-practice chatbot testing tool Botium on five Amazon Lex chatbots, consistently outperforming it in all subjects, generating more tests with nearly double complexity, achieving overall 83-95% coverage of conversational elements, and improving fault detection effectiveness by up to four times at comparable time costs.  \nKeywords: Task-based Chatbot · Model-based Testing · Amazon Lex  \n1 Introduction  \nIn recent years, task-based chatbots, i.e., software designed to deliver functionalities through conversations [7, 8, 27], have gained popularity due to their integration into real-world applications. Advancements in technology have made them ubiquitous in a multitude of domains, including e-commerce, healthcare, and customer help-desk [20, 21, 25] and a large number of design platforms have emerged, with Dialogflow, Amazon Lex, Rasa, Microsoft Bot, and IBM Watson being systematically acknowledged as the most popular platforms [6, 10, 36, 37], in both open-source and commercial settings.  \nGiven chatbot pervasiveness, tailored approaches to ensure their quality have recently emerged, yet raising novel challenges [15, 23, 31, 32] . Unlike traditional testing approaches that validate the software, for instance, based on API calls or  \n⋆ Corresponding Author  \n2 D. Clerissi et al.  \nGUI actions, chatbots combine conventional software layers managing business logic with conversational interfaces that must interpret user requests through natural-language interactions, whose responses must also be interpreted to determine whether a task was correctly performed, raising issues in terms of oracles.  \nInitial efforts in chatbot quality assurance targeted the speech recognition module [9,30] and non-functional aspects [3] . Later, testing chatbots as a whole was addressed by employing input mutation for robustness testing [28,33,39] and metamorphic testing [18] to address the oracle problem [11, 13] . However, these proposals demand extensive manual intervention and deep prior knowledge of the chatbot behavior.  \nBotium is the state-of-the-practice framework for automated end-to-end task-based chatbot testing [2], originally developed by Botium GmbH (now Cyara) . Botium provides connectors to several widely used chatbot design platforms and frameworks. Subsequent work has exploited Botium to achieve advances in testing, particularly in test diversity [14, 38] and improved test coverage [16] . Still, all these tools present limitations in test effectiveness, as tests by construction are limited to single request-response interactions, produce only regression oracles, and are susceptible to flakiness 1 [19, 24, 34, 35, 38] . Further, all the tools extending Botium only address Dialogflow and Rasa as chatbot development platforms, thu","cbCaiaYl3jFCiicZ","https://ap.wps.com/l/cbCaiaYl3jFCiicZ","pdf",1033623,1,16,"English","en",105,"# Introduction\n# Background\n# LexTester\n# Experiment\n# Related Work\n# Final Remarks","[{\"question\":\"What problem does the paper address in testing task-based chatbots?\",\"answer\":\"Task-based chatbots can contain bugs, and existing assessment methods are limited by simplistic test scenarios and weaknesses in oracles that decide whether tasks were correctly completed.\"},{\"question\":\"How does LexTester generate its tests for Amazon Lex chatbots?\",\"answer\":\"LexTester explores the chatbot’s conversational space to derive a Dialog Graph of all possible interactions, then generates an executable test suite according to selected coverage strategies.\"},{\"question\":\"How does LexTester perform compared with Botium?\",\"answer\":\"Across five Amazon Lex chatbots, LexTester consistently outperforms Botium, producing more tests with nearly double complexity, achieving 83–95% coverage of conversational elements, and improving fault detection effectiveness by up to four times at comparable time 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problem does the paper address in testing task-based chatbots?","Question",{"text":75,"@type":76},"Task-based chatbots can contain bugs, and existing assessment methods are limited by simplistic test scenarios and weaknesses in oracles that decide whether tasks were correctly completed.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does LexTester generate its tests for Amazon Lex chatbots?",{"text":80,"@type":76},"LexTester explores the chatbot’s conversational space to derive a Dialog Graph of all possible interactions, then generates an executable test suite according to selected coverage strategies.",{"name":82,"@type":73,"acceptedAnswer":83},"How does LexTester perform compared with Botium?",{"text":84,"@type":76},"Across five Amazon Lex chatbots, LexTester consistently outperforms Botium, producing more tests with nearly double complexity, achieving 83–95% coverage of conversational elements, and improving fault detection effectiveness by up to four times at comparable 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