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records:49847 a azonOnto:Article ;
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    azonOnto:authorInfo [ a azonOnto:AuthorInfo ;
            azonOnto:author person:12978 ;
            azonOnto:position 1 ],
        [ a azonOnto:AuthorInfo ;
            azonOnto:author person:12968 ;
            azonOnto:position 2 ] ;
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        collection:11 ;
    azonOnto:description "Bugs are the inevitable part of a software system. Nowadays, large software development projectseven release beta versions of their products to gather bug reports from users. The collected bugreports are then worked upon by various developers in order to resolve the defects and make thefinal software product more reliable. The high frequency of incoming bugs makes the bug handlinga difficult and time consuming task. Bug assignment is an integral part of bug triaging that aimsat the process of assigning a suitable developer for the reported bug who corrects the source codein order to resolve the bug. There are various semi and fully automated techniques to ease thetask of bug assignment. This paper presents the current state of the art of various techniquesused for bug report assignment. Through exhaustive research, the authors have observed that machine learning and information retrieval based bug assignment approaches are most popular inliterature. A deeper investigation has shown that the trend of techniques is taking a shift frommachine learning based approaches towards information retrieval based approaches. Therefore, thefocus of this work is to find the reason behind the observed drift and thus a comparative analysisis conducted on the bug reports of the Mozilla, Eclipse, Gnome and Open Office projects in theBugzilla repository. The results of the study show that the information retrieval based techniqueyields better efficiency in recommending the developers for bug reports."@en ;
    azonOnto:destinationGroup "naukowcy"@pl,
        "ogół społeczeństwa"@pl,
        "studenci"@pl ;
    azonOnto:doi [ a azonOnto:IdentifierInfo ;
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            azonOnto:value "10.5277/e-Inf170106"^^xsd:string ] ;
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            azonOnto:name "e-Informatica_Vol.11_2017_Issue_1_Art_6.zip"^^xsd:string ;
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            azonOnto:name "informatyka"@pl ;
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            azonOnto:name "adaptacja"@pl ;
            azonOnto:uri "http://plwordnet.pwr.wroc.pl/wordnet/synset/14176"^^xsd:anyURI ] ;
    azonOnto:language "Angielski"^^xsd:string ;
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            azonOnto:name "ID-NC-ND"^^xsd:string ;
            azonOnto:uri "https://azon.e-science.pl/licencje/ID-NC-ND_PWr.pdf"^^xsd:anyURI ] ;
    azonOnto:numeration "Vol. 11, issue 1"^^xsd:string ;
    azonOnto:pageFrom 125 ;
    azonOnto:pageTo 149 ;
    azonOnto:partner [ a azonOnto:Organization ;
            azonOnto:name "Politechnika Wrocławska"^^xsd:string ] ;
    azonOnto:publicationYear 2017 ;
    azonOnto:publisher "Oficyna Wydawnicza Politechniki Wrocławskiej"^^xsd:string ;
    azonOnto:relatedLink [ a azonOnto:RelatedLinkInfo ;
            azonOnto:name "e-Informatica Software Engineering Journal, Vol. 11, 2017"^^xsd:string ;
            azonOnto:uri "https://www.dbc.wroc.pl/dlibra/publication/41451/edition/37397"^^xsd:anyURI ] ;
    azonOnto:scientificDiscipline "dziedzina nauk technicznych / informatyka (2011)"@pl ;
    azonOnto:source [ a azonOnto:Source ;
            azonOnto:title "e-Informatica: Software Engineering Journal"@pl ] ;
    azonOnto:submitter [ a azonOnto:Person ;
            azonOnto:name "Magdalena Kruczek"^^xsd:string ] ;
    azonOnto:title "Machine learning or information retrieval techniques for bug triaging: which is better?"@pl .

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