[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128001-en":3,"doc-seo-128001-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128001,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Combining Metamorphic Testing and Machine Learning to Enhance OpenStreetMap","Metamorphic testing (MT) supports validation when no oracle exists by defining metamorphic relations (MRs) that connect source inputs with expected output properties. For OpenStreetMap (OSM), MR violations can reveal many mapping errors but may also generate non-negligible false positives that require expert manual review. The presented approach addresses this bottleneck by automatically discarding false positives through a machine-learning extension. Three random-forest-based models, one per MR, are trained on real city map data across continents and evaluated on unseen countries, achieving accuracy at least 0.90 and often near 1.0, with consistent F1-scores.","Combining Metamorphic Testing and Machine Learning to Enhance OpenStreetMap  \nManuel Méndez , Antonio Becerra-Terón , Jesús M. Almendros-Jiménez , Mercedes G. Merayo  ,  \nand Manuel Núñez   \nAbstract—Metamorphic testing (MT) is a useful tool to test systems where an oracle is not available. MT relies on the definition of metamorphic relations (MR), that is, certain properties that relate a set of inputs and the set of outputs produced by the system under test (SUT) as response to these inputs. Usually, a violation of an MR implies that the SUT is faulty. However, some work on MT accepts, for certain SUTs, that the violation of an MR almost always is a symptom of an error but assumes the potential existence of false positives. This is the case, for instance, of our recent work where we applied MT to improve OpenStreetMap (OSM). Our MRs were able to uncover a large amount of errors in all the analyzed maps but we suffered the presence of a nonnegligible number of false positives. Therefore, an expert had to manually check the suspicious elements identified by our MRs. If we analyze large maps, then this manual task is unfeasible. In this article we solve the main limitation of our previous approach: we accurately and automatically discard false positives. Our new framework combines MT, along the same lines of our previous work, and machine learning. Specifically, we provide three models, one for each MR, based on the random forest model. The models were extensively trained with real data obtained from the application of MRs to maps of cities located in different continents. In order to evaluate the usefulness of our models, we tested them using different cities, in countries that were not considered in the training set. The results were very good: accuracy of the models is never lower than 0.90, it is usually much higher and in many situations reaches 1.0. The computation of the F1-scores yielded similar results.  \nIndex Terms—Machine learning (ML), metamorphic testing (MT), openstreetmap (OSM), quality of maps.  \nI. INTRODUCTION  \nsaeiocofidrspfutwhthsee, taphesylt themobtThaitoneduiypnsuuorrnrelteonnttno(SoTlicl)de,  \nN CLASSICAL software testing [1],[2], the tester selects a  \nManuscript received 30 September 2022; revised 1 May 2023, 24 October 2023, and 17 January 2024; accepted 12 March 2024 . Date of publication 4 April 2024; date of current version 3 December 2024 . This work was supported by the State Research Agency (AEI) of the Spanish Ministry of Science and Innovation, under Grant PID2019-104735RB-C42 (SAFER) and Grant PID2021-122215NB-C31(AwESOMe). Associate Editor: H. Jiang. (Cor  \nresponding author: Manuel Núñez.)  \nManuelMéndez,Mercedes G. Merayo, andManuelNúñez are withtheDesign and Testing of Reliable Systems research group, Universidad Complutense de Madrid, 28040 Madrid, Spain ([e-mail: manumend@ucm.es](e-mail: manumend@ucm.es); mgmerayo@ [fdi.ucm.es](fdi.ucm.es); [mn@sip.ucm.es](mn@sip.ucm.es)).  \nAntonio Becerra-Terón and Jesús M. Almendros-Jiménez are with the Department of Informatics, Carretera de Sacramento s/n, University of Almería, 04120 Almería, Spain (e-mail: [abecerra@ual.es](abecerra@ual.es); [jalmen@ual.es](jalmen@ual.es)).  \nData is available online at [https://github.com/jalmenUAL/MT-OSM](https://github.com/jalmenUAL/MT-OSM). Digital Object Identifier 10.1109/TR.2024.3379366  \nthat will be in charge of providing a verdict about the correctness of the observed behaviors. Unfortunately, oracles are not always available [3] and, in this case, the testing process mainly relies on the experience and knowledge of the tester about the expected behavior of the SUT. This process is mainly manual, time-consuming, and cannot be applied to complex systems. Moreover, in some situations even an expert tester would be unable to provide a verdict. For example, consider that a program P is an implementation of the sine function. If we are going to test this program with an input such as 43, what is the expected r","cbCaiuOZrxNhLxJH","https://ap.wps.com/l/cbCaiuOZrxNhLxJH","pdf",2368727,4,1,15,"English","en",105,"# Abstract\n# Index Terms\n# Introduction\n## Classical software testing and oracle limitation\n## Metamorphic testing concepts (MRs, source and follow-up inputs)\n## OpenStreetMap representation and prior MT framework","[{\"question\":\"Why is metamorphic testing useful when an oracle is unavailable?\",\"answer\":\"MT defines metamorphic relations (MRs) that describe properties linking multiple inputs to their outputs, enabling correctness checks without needing the exact expected output for each input.\"},{\"question\":\"What problem occurred when applying MT to OpenStreetMap?\",\"answer\":\"MR violations uncovered many map errors, but a significant number of false positives were produced, requiring experts to manually inspect suspicious elements.\"},{\"question\":\"How does the proposed framework remove false positives in OSM analysis?\",\"answer\":\"It combines MT with machine learning by training three random-forest models—one for each MR—to classify and discard false positives automatically, achieving strong accuracy on unseen cities.\"}]","Combining Metamorphic Testing and Machine Learning to Enhance OpenStreetMap | 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is metamorphic testing useful when an oracle is unavailable?","Question",{"text":76,"@type":77},"MT defines metamorphic relations (MRs) that describe properties linking multiple inputs to their outputs, enabling correctness checks without needing the exact expected output for each input.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What problem occurred when applying MT to OpenStreetMap?",{"text":81,"@type":77},"MR violations uncovered many map errors, but a significant number of false positives were produced, requiring experts to manually inspect suspicious elements.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed framework remove false positives in OSM analysis?",{"text":85,"@type":77},"It combines MT with machine learning by training three random-forest models—one for each MR—to classify and discard false positives automatically, achieving strong accuracy on unseen 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