[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81923-en":3,"doc-seo-81923-105":29,"detail-sidebar-cat-0-en-105":82},{"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":4,"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},81923,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Intent-Based Mutation Testing","Intent-based mutation testing generates mutants by rewriting the programming intents implemented in programs, rather than by applying small syntactic changes to code. Each mutated intent corresponds to a slightly different objective, producing mutant behaviors that reflect corner cases and misunderstandings of program specifications. Implemented with Large Language Models, the approach transforms natural-language intents into diverse and complex mutants, evaluated on 29 programs. Results show syntactic complexity, semantic diversity, and 55% of intent mutations not covered by traditional mutation, indicating strong complementarity.","Intent-Based Mutation Testing: From Naturally Written Programming Intents to Mutants  \nAsma Hamidi  \nSnT, University of Luxembourg Luxembourg [asma.hamidi@uni.lu](asma.hamidi@uni.lu)  \nAhmed Khanfir  \nMedtech, South Mediterranean University Tunis, Tunisia [ahmed.khanfir@medtech.tn](ahmed.khanfir@medtech.tn)  \nMike Papadakis  \nSnT, University of Luxembourg Luxembourg [michail.papadakis@uni.lu](michail.papadakis@uni.lu)  \narXiv :2607 .05 149v 1 [ cs . SE] 6 Jul 2026  \nAbstract—This paper presents intent-based mutation testing, a testing approach that generates mutations by changing the programming intents that are implemented in the programs under test. In contrast to traditional mutation testing, which changes (mutates) the way programs are written, intent mutation changes (mutates) the behavior of the programs by producing mutations that implement (slightly) different intents than those implemented in the original program. The mutations of the programming intents represent possible corner cases and misunderstandings of the program behavior, i.e., program specifications, and thus can capture different classes of faults than traditional (syntaxbased) mutation. Moreover, since programming intents can be implemented in different ways, intent-based mutation testing can generate diverse and complex mutations that are close to the original programming intents (specifications) and thus direct testing towards the intent variants of the program behavior/specifications. We implement intent-based mutation testing using Large Language Models (LLMs) that mutate programming intents and transform them into mutants. We evaluate intentbased mutation on 29 programs and show that it generates mutations that are syntactically complex, semantically diverse, and quite different (semantically) from the traditional ones. We also show that 55% of the intent-based mutations are not subsumed by traditional mutations. Overall, our analysis shows that intent-based mutation testing can be a powerful complement to traditional (syntax-based) mutation testing.  \nI. INTRODUCTION  \nMutation testing has long been recognized as one of the most powerful testing techniques [1],[2] . It generates program variants by altering the way programs are written, i.e., by making simple syntactic changes to the code under test. These variants are then used as targets for differential program analysis, that is, test writing (or test selection) with the aim to distinguish the behavior of the original program from that of the variants. When a test triggers a difference in the behavior of the mutant and the original programs, the mutant is considered as covered, called ’killed’, otherwise is considered as not covered and called ’live’. The effectiveness of the test suites is then measured by the mutation score, the proportion of mutants killed over all considered mutants [2] .  \nTraditional mutation testing operates at the program syntax level and thus is typically oriented toward errors that are syntactically small, i.e., the syntactic distance of the variants to the original program is rather small. For example, a typical mutation is to replace one operator such as ’>’ with another’>=’ in a relational expression. This approach allows the  \nintroduction of subtle semantic deviations that make mutation effective at testing the behavioral boundaries of the programs under test. At the same time, this approach limits testing to the program logic that is actually implemented, making mutation testing less effective in revealing complex behavior-oriented (falling on the core of business logic) and omission faults [1] .  \nTo address these issues, we propose a novel approach to mutation testing, namely intent-based mutation testing. An intent is the programmer’s objective for the code, described informally in natural language and offers a description of the task that is implemented. For example, if a programmer intends to create a function that calculates the factorial of a number, the in","cbCaimNImuFr7QnQ","https://ap.wps.com/l/cbCaimNImuFr7QnQ","pdf",427424,1,11,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What experimental findings support the value of the approach?\",\"answer\":\"Evaluation on 29 programs shows mutants with syntactic complexity and semantic diversity, and 55% of intent-based mutations are not subsumed by traditional mutations, making it a powerful complement.\"}]",1784177072,28,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":27},"intent-based-mutation-testing","",{"@graph":35,"@context":76},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/intent-based-mutation-testing/81923/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"What experimental findings support the value of the approach?","Question",{"text":74,"@type":75},"Evaluation on 29 programs shows mutants with syntactic complexity and semantic diversity, and 55% of intent-based mutations are not subsumed by traditional mutations, making it a powerful complement.","Answer","https://schema.org",{"og:url":51,"og:type":78,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":80,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":83},[84,88,92,96,101,106,111,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":45,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":45,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":45,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general"]