{"id":"https://openalex.org/W7133329644","doi":"https://doi.org/10.48550/arxiv.2603.01409","title":"MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning","display_name":"MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133329644","doi":"https://doi.org/10.48550/arxiv.2603.01409"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01409","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01409","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01409","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127951427","display_name":"Sicheng Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhu, Sicheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127919191","display_name":"Jiajun Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiajun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127948142","display_name":"Jiawei Ai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ai, Jiawei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127972420","display_name":"Xin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5127951427"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.366100013256073,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.366100013256073,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10743","display_name":"Software Testing and Debugging Techniques","score":0.31630000472068787,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.04769999906420708,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/test-suite","display_name":"Test suite","score":0.7490000128746033},{"id":"https://openalex.org/keywords/unit-testing","display_name":"Unit testing","score":0.6797999739646912},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.558899998664856},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5152999758720398},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5051000118255615},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4936999976634979},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.44699999690055847},{"id":"https://openalex.org/keywords/sequential-analysis","display_name":"Sequential analysis","score":0.4140999913215637},{"id":"https://openalex.org/keywords/code-coverage","display_name":"Code coverage","score":0.4083000123500824}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.795199990272522},{"id":"https://openalex.org/C151552104","wikidata":"https://www.wikidata.org/wiki/Q7705809","display_name":"Test suite","level":4,"score":0.7490000128746033},{"id":"https://openalex.org/C148027188","wikidata":"https://www.wikidata.org/wiki/Q907375","display_name":"Unit testing","level":3,"score":0.6797999739646912},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.558899998664856},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5152999758720398},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5134000182151794},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5051000118255615},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4936999976634979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4684000015258789},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.44699999690055847},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.4140999913215637},{"id":"https://openalex.org/C53942775","wikidata":"https://www.wikidata.org/wiki/Q1211721","display_name":"Code coverage","level":3,"score":0.4083000123500824},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.40700000524520874},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C501734568","wikidata":"https://www.wikidata.org/wiki/Q42918","display_name":"Mutation","level":3,"score":0.34860000014305115},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33000001311302185},{"id":"https://openalex.org/C128942645","wikidata":"https://www.wikidata.org/wiki/Q1568346","display_name":"Test case","level":3,"score":0.32269999384880066},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.32120001316070557},{"id":"https://openalex.org/C165825675","wikidata":"https://www.wikidata.org/wiki/Q1399743","display_name":"Model-based testing","level":4,"score":0.3174000084400177},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2667999863624573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01409","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01409","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.01409","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01409","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.7715649008750916,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"often":[4],"fail":[5],"to":[6,22,79,113],"generate":[7],"correct":[8],"code":[9,165,182],"on":[10,124,168],"the":[11,24,27,77,111,143,173,188],"first":[12],"attempt,":[13],"which":[14,109,162],"requires":[15],"using":[16],"generated":[17],"unit":[18],"tests":[19,157],"as":[20,85,159],"verifiers":[21],"validate":[23],"solutions.":[25],"Despite":[26],"success":[28],"of":[29,145],"recent":[30],"verification":[31],"methods,":[32],"they":[33],"remain":[34],"constrained":[35],"by":[36,148,170],"a":[37,45,73,86,100,136],"\"scaling-by-quantity\"":[38],"paradigm.":[39],"This":[40],"brute-force":[41],"approach":[42],"suffers":[43],"from":[44],"critical":[46],"limitation:":[47],"it":[48,118],"yields":[49],"diminishing":[50],"returns":[51],"in":[52,187],"fault":[53],"detection":[54],"while":[55,117,141],"causing":[56],"severe":[57],"test":[58,83,146],"redundancy.":[59],"To":[60],"address":[61],"this,":[62],"we":[63,98,151],"propose":[64],"MIST-RL":[65,130],"(Mutation-based":[66],"Incremental":[67],"Suite":[68],"Testing":[69],"via":[70,91],"Reinforcement":[71],"Learning),":[72],"framework":[74],"that":[75,129,153],"shifts":[76],"focus":[78],"\"scaling-by-utility\".":[80],"We":[81],"formulate":[82],"generation":[84],"sequential":[87],"decision":[88],"process":[89],"optimized":[90],"Group":[92],"Relative":[93],"Policy":[94],"Optimization":[95],"(GRPO).":[96],"Specifically,":[97],"introduce":[99],"novel":[101],"incremental":[102],"mutation":[103,139],"reward":[104],"combined":[105],"with":[106,176],"dynamic":[107],"penalties,":[108],"incentivizes":[110],"model":[112],"discover":[114],"new":[115],"faults":[116],"suppresses":[119],"functionally":[120],"equivalent":[121],"assertions.":[122],"Experiments":[123],"HumanEval+":[125,169],"and":[126,183],"MBPP+":[127],"demonstrate":[128],"outperforms":[131],"state-of-the-art":[132],"baselines.":[133],"It":[134],"achieves":[135],"+28.5%":[137],"higher":[138],"score":[140],"reducing":[142],"number":[144],"cases":[147],"19.3%.":[149],"Furthermore,":[150],"show":[152],"these":[154],"compact,":[155],"high-utility":[156],"serve":[158],"superior":[160],"verifiers,":[161],"improves":[163],"downstream":[164],"reranking":[166],"accuracy":[167],"3.05%":[171],"over":[172],"SOTA":[174],"baseline":[175],"10":[177],"candidate":[178],"samples.":[179],"The":[180],"source":[181],"data":[184],"are":[185],"provided":[186],"supplementary":[189],"material.":[190]},"counts_by_year":[],"updated_date":"2026-03-04T07:09:34.246503","created_date":"2026-03-04T00:00:00"}