Handling class imbalance problem in software maintainability prediction: an empirical investigation

被引:4
|
作者
Malhotra, Ruchika [1 ]
Lata, Kusum [1 ,2 ]
机构
[1] Delhi Technol Univ, Dept Comp Sci & Engn, Discipline Software Engn, Delhi 110042, India
[2] Delhi Technol Univ, Univ Sch Management & Entrepreneurship, East Delhi Campus, Delhi 110095, India
关键词
software maintenance; software maintainability; imbalanced learning; DEFECT PREDICTION; NEURAL-NETWORKS; CODE CLONES; CLASSIFICATION; FRAMEWORK; TRACKING; MACHINE; MODELS;
D O I
10.1007/s11704-021-0127-0
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes' prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] A Relabeling Approach to Handling the Class Imbalance Problem for Logistic Regression
    Li, Yazhe
    Adams, Niall
    Bellotti, Tony
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2022, 31 (01) : 241 - 253
  • [32] Handling the Multi-Class Imbalance Problem using ECOC
    Valdovinos Rosas, Rosa Maria
    Abad Sanchez, Rosalinda
    Alejo Eleuterio, Roberto
    Herrera Arteaga, Edgar
    Trueba Espinosa, Adrian
    COMPUTACION Y SISTEMAS, 2013, 17 (04): : 583 - 592
  • [33] Handling Class Imbalance Problem using Oversampling Techniques: A Review
    Gosain, Anjana
    Sardana, Saanchi
    2017 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2017, : 79 - 85
  • [34] Efficient Approach to Employee Attrition Prediction by Handling Class Imbalance
    Prathilothamai, M.
    Sudarshana
    Maheswari, A. Sri Sakthi
    Chandravadhana, A.
    Goutham, R.
    ADVANCES IN COMPUTING AND DATA SCIENCES (ICACDS 2022), PT II, 2022, 1614 : 263 - 277
  • [35] Using Class Imbalance Learning for Software Defect Prediction
    Wang, Shuo
    Yao, Xin
    IEEE TRANSACTIONS ON RELIABILITY, 2013, 62 (02) : 434 - 443
  • [36] Class Imbalance in Software Fault Prediction Data Set
    Arun, C.
    Lakshmi, C.
    ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY COMPUTATIONS IN ENGINEERING SYSTEMS, 2020, 1056 : 745 - 757
  • [37] Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance
    Bejjanki, Kiran Kumar
    Gyani, Jayadev
    Gugulothu, Narsimha
    SYMMETRY-BASEL, 2020, 12 (03):
  • [38] Class Imbalance Issue in Software Defect Prediction Models by various Machine Learning Techniques: An Empirical Study
    Pandey, Sushant Kumar
    Tripathi, Anil Kumar
    2021 8TH INTERNATIONAL CONFERENCE ON SMART COMPUTING AND COMMUNICATIONS (ICSCC), 2021, : 58 - 63
  • [39] An empirical study of software change classification with imbalance data-handling methods
    Zhu, Xiaoyan
    Niu, Binbin
    Whitehead, E. James, Jr.
    Sun, Zhongbin
    SOFTWARE-PRACTICE & EXPERIENCE, 2018, 48 (11): : 1968 - 1999
  • [40] Selecting target concept in one-class classification for handling class imbalance problem
    Perez-Sanchez, Beatriz
    Fontenla-Romero, Oscar
    Sanchez-Marono, Noelia
    2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2015,