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ProRE: An ACO- based programmer recommendation model to precisely manage software bugs

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10251685" target="_blank" >RIV/61989100:27240/23:10251685 - isvavai.cz</a>

  • Výsledek na webu

    <a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85146751385&origin=resultslist&sort=plf-f&src=s&st1=rezac+f&sid=a85db75dc11a8dcd04fa1cd71ca515c2&sot=b&sdt=b&sl=20&s=AUTHOR-NAME%28rezac+f%29&relpos=0&citeCnt=0&searchTerm=" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85146751385&origin=resultslist&sort=plf-f&src=s&st1=rezac+f&sid=a85db75dc11a8dcd04fa1cd71ca515c2&sot=b&sdt=b&sl=20&s=AUTHOR-NAME%28rezac+f%29&relpos=0&citeCnt=0&searchTerm=</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.jksuci.2022.12.017" target="_blank" >10.1016/j.jksuci.2022.12.017</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    ProRE: An ACO- based programmer recommendation model to precisely manage software bugs

  • Popis výsledku v původním jazyce

    The process of assigning bugs to particular programmers is called bug assignment in software engineering. The programmer can fix the bugs by applying their knowledge. This research article presents an Ant colony optimization-based programmer recommendation model (ProRE) to manage software bugs precisely. The proposed ProRE model performs four operations: data pre-processing, i.e., data Pre-processing, extraction, feature selection, and programmer recommendation process. The feature selection stage utilized the Ant colony optimization (ACO) method to determine the appropriate subsets of features from all features. In the programmer recommendation stages, three programmer metrics, i.e., functionality ranking, bug occurrence, and mean Bug fixing time, are utilized for the recommendation assignment. The effectiveness of the proposed programmer recommendation system is assessed using datasets from Mozilla, Eclipse, Firefox, JBoss, and OpenFOAM. It is noted that the proposed model offers a better recommendation strategy over the other available systems. The simulation findings of the proposed ProRE model are also analyzed with well-known available ML methods, i.e., SVM, NB, and C4.5. It is observed that the recommendation results have improved by an average of 4%, 10%, and 12% compared to SVM, C4.5, and NB-based models. Programmer recommendation software is implemented for allocating the bugs to accurate programmers. It has been found that the proposed ProRE model generates more optimistic outcomes than existing ones.

  • Název v anglickém jazyce

    ProRE: An ACO- based programmer recommendation model to precisely manage software bugs

  • Popis výsledku anglicky

    The process of assigning bugs to particular programmers is called bug assignment in software engineering. The programmer can fix the bugs by applying their knowledge. This research article presents an Ant colony optimization-based programmer recommendation model (ProRE) to manage software bugs precisely. The proposed ProRE model performs four operations: data pre-processing, i.e., data Pre-processing, extraction, feature selection, and programmer recommendation process. The feature selection stage utilized the Ant colony optimization (ACO) method to determine the appropriate subsets of features from all features. In the programmer recommendation stages, three programmer metrics, i.e., functionality ranking, bug occurrence, and mean Bug fixing time, are utilized for the recommendation assignment. The effectiveness of the proposed programmer recommendation system is assessed using datasets from Mozilla, Eclipse, Firefox, JBoss, and OpenFOAM. It is noted that the proposed model offers a better recommendation strategy over the other available systems. The simulation findings of the proposed ProRE model are also analyzed with well-known available ML methods, i.e., SVM, NB, and C4.5. It is observed that the recommendation results have improved by an average of 4%, 10%, and 12% compared to SVM, C4.5, and NB-based models. Programmer recommendation software is implemented for allocating the bugs to accurate programmers. It has been found that the proposed ProRE model generates more optimistic outcomes than existing ones.

Klasifikace

  • Druh

    J<sub>SC</sub> - Článek v periodiku v databázi SCOPUS

  • CEP obor

  • OECD FORD obor

    20203 - Telecommunications

Návaznosti výsledku

  • Projekt

  • Návaznosti

    S - Specificky vyzkum na vysokych skolach

Ostatní

  • Rok uplatnění

    2023

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název periodika

    Journal of King Saud University - Computer and Information Sciences

  • ISSN

    1319-1578

  • e-ISSN

    2213-1248

  • Svazek periodika

    35

  • Číslo periodika v rámci svazku

    1

  • Stát vydavatele periodika

    SA - Království Saúdská Arábie

  • Počet stran výsledku

    16

  • Strana od-do

    483-498

  • Kód UT WoS článku

  • EID výsledku v databázi Scopus

    2-s2.0-85146751385