A Comprehensive Taxonomy for Prediction Models in Software Engineering

被引:1
|
作者
Yang, Xinli [1 ]
Liu, Jingjing [1 ]
Zhang, Denghui [1 ]
机构
[1] Zhejiang Shuren Univ, Coll Informat Sci & Technol, Hangzhou 310015, Peoples R China
关键词
artificial intelligence; prediction model; software engineering; comprehensive taxonomy; PERFORMANCE PREDICTION; DEFECT PREDICTION; FRAMEWORK; ACCURACY; METRICS;
D O I
10.3390/info14020111
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Applying prediction models to software engineering is an interesting research area. There have been many related studies which leverage prediction models to achieve good performance in various software engineering tasks. With more and more researches in software engineering leverage prediction models, there is a need to sort out related studies, aiming to summarize which software engineering tasks prediction models can apply to and how to better leverage prediction models in these tasks. This article conducts a comprehensive taxonomy on prediction models applied to software engineering. We review 136 papers from top conference proceedings and journals in the last decade and summarize 11 research topics prediction models can apply to. Based on the papers, we conclude several big challenges and directions. We believe that the comprehensive taxonomy will help us understand the research area deeper and infer several useful and practical implications.
引用
收藏
页数:32
相关论文
共 50 条
  • [1] Empathy models and software engineering-A preliminary analysis and taxonomy
    Gunatilake, Hashini
    Grundy, John
    Mueller, Ingo
    Hoda, Rashina
    JOURNAL OF SYSTEMS AND SOFTWARE, 2023, 203
  • [2] Predictive Models in Software Engineering: Challenges and Opportunities
    Yang, Yanming
    Xia, Xin
    Lo, David
    Bi, Tingting
    Grundy, John
    Yang, Xiaohu
    ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 2022, 31 (03)
  • [3] On the dataset shift problem in software engineering prediction models
    Burak Turhan
    Empirical Software Engineering, 2012, 17 : 62 - 74
  • [4] On the dataset shift problem in software engineering prediction models
    Turhan, Burak
    EMPIRICAL SOFTWARE ENGINEERING, 2012, 17 (1-2) : 62 - 74
  • [5] Comprehensive Model for Software Fault Prediction
    Singh, Pradeep
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTING AND INFORMATICS (ICICI 2017), 2017, : 1103 - 1108
  • [6] A COMPREHENSIVE SURVEY OF PETRI NET MODELING IN SOFTWARE ENGINEERING
    He, Xudong
    INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING, 2013, 23 (05) : 589 - 625
  • [7] Multilabel classification for defect prediction in software engineering
    Pachouly, Jalaj
    Ahirrao, Swati
    Kotecha, Ketan
    Kulkarni, Ambarish
    Alfarhood, Sultan
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [8] A Taxonomy of Software Engineering Challenges for Machine Learning Systems: An Empirical Investigation
    Lwakatare, Lucy Ellen
    Raj, Aiswarya
    Bosch, Jan
    Olsson, Helena Holmstrom
    Crnkovic, Ivica
    AGILE PROCESSES IN SOFTWARE ENGINEERING AND EXTREME PROGRAMMING, XP 2019, 2019, 355 : 227 - 243
  • [9] A Taxonomy of Data Quality Challenges in Empirical Software Engineering
    Bosu, Michael Franklin
    MacDonell, Stephen G.
    2013 22ND AUSTRALASIAN CONFERENCE ON SOFTWARE ENGINEERING (ASWEC), 2013, : 97 - 106
  • [10] A Taxonomy of Techniques for SLO Failure Prediction in Software Systems
    Grohmann, Johannes
    Herbst, Nikolas
    Chalbani, Avi
    Arian, Yair
    Peretz, Noam
    Kounev, Samuel
    COMPUTERS, 2020, 9 (01)