Deep Learning-Based Resolution Prediction of Software Enhancement Reports

被引:2
作者
Arshad, Muhammad Ali [1 ]
Huang, Zhiqiu [2 ]
Riaz, Adnan [3 ]
Hussain, Yasir [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut NUAA, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut NUAA, Key Lab Safety Crit Software, Minist Ind & Informat Technol, Nanjing 211106, Peoples R China
[3] Dalian Univ Technol DUT, Sch Comp Sci & Technol, Dalian 116024, Peoples R China
来源
2021 IEEE 11TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC) | 2021年
关键词
machine learning; document classification; enhancement reports; natural language processing; computational intelligence; SEVERITY PREDICTION; APPROVAL PREDICTION;
D O I
10.1109/CCWC51732.2021.9375841
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The automatic resolution prediction of newly submitted enhancement reports is an important task during the bug triage process. It can help developers automatically predict the resolution status of enhancement reports. The resolution prediction is still a manual process which is very time-consuming, costly, and laborious. To help software applications for the timely implementation of enhancement reports, we introduce a deep learning-based technique to predict the resolution of newly submitted enhancement reports automatically by using a summary and description of enhancement reports. We use Word2Vec and a deep-learning-based classifier that can learn the deep syntactical and semantical relationship between the words of enhancement reports. We use additional novel features from enhancement reports and customized tokenizer to save useful features. Experimental results show the proposed approach enhances the performance as compared to state-of-the-art approaches in resolution prediction and has an effective ability to predict the resolution status of enhancement reports.
引用
收藏
页码:492 / 499
页数:8
相关论文
共 50 条
  • [31] An integrated deep learning-based approach for automobile maintenance prediction with GIS data
    Chen, Chong
    Liu, Ying
    Sun, Xianfang
    Di Cairano-Gilfedder, Carla
    Titmus, Scott
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2021, 216
  • [32] Optimal Histopathological Magnification Factors for Deep Learning-Based Breast Cancer Prediction
    Ashtaiwi, Abduladhim
    APPLIED SYSTEM INNOVATION, 2022, 5 (05)
  • [33] Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review
    Mozaffari, Sajjad
    Al-Jarrah, Omar Y.
    Dianati, Mehrdad
    Jennings, Paul
    Mouzakitis, Alexandros
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (01) : 33 - 47
  • [34] Deep Learning for Software Defect Prediction in time
    Yadav, Monika
    Singh, Vijendra
    Rastogi, Priyanka
    2018 FIFTH INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND GRID COMPUTING (IEEE PDGC), 2018, : 7 - 12
  • [35] Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia
    Zhang He
    Yin Mengting
    Liu Qianhui
    Ding Fei
    Hou Lisha
    Deng Yiping
    Cui Tao
    Han Yixian
    Pang Weiguang
    Ye Wenbin
    Yue Jirong
    He Yong
    中华医学杂志英文版, 2023, 136 (08)
  • [36] Deep learning-based hotspot prediction of Via printability in process window corners
    Selvam, Punitha
    Rezaeifakhr, Pouya
    Schroeder, Uwe Paul
    Bakshi, Janam
    Mohamed, Omnia
    Batarseh, Fadi
    Madhavan, Sriram
    DESIGN-PROCESS-TECHNOLOGY CO-OPTIMIZATION XV, 2021, 11614
  • [37] Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector
    Domingos, Edvaldo
    Ojeme, Blessing
    Daramola, Olawande
    COMPUTATION, 2021, 9 (03)
  • [38] Deep learning-based identification of drying methods and quality prediction of Dendrobium officinale
    Li, Guangyao
    Duan, Zhili
    Wang, Yuanzhong
    MICROCHEMICAL JOURNAL, 2025, 213
  • [39] Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments
    Mennes, Ruben
    Claeys, Maxim
    De Figueiredo, Felipe A. P.
    Jabandzic, Irfan
    Moerman, Ingrid
    Latre, Steven
    IEEE ACCESS, 2019, 7 : 45818 - 45830
  • [40] Deep Learning-based QoS Prediction with Innate Knowledge of the Radio Access Network
    Perdomo, Jose
    Kousaridas, Apostolos
    Zhou, Chan
    Monserrat, Jose F.
    2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,