Analysis of Software Developers' Programming Language Preferences and Community Behavior From Big5 Personality Traits

被引:0
作者
Mukta, Md. Saddam Hossain [1 ]
Antu, Badrun Nessa [2 ]
Azad, Nasreen [1 ]
Abedeen, Iftekharul [2 ]
Islam, Najmul [1 ]
机构
[1] Lappeenranta Lahti Univ Technol, LUT Sch Engn Sci, Lappeenranta, Finland
[2] United Int Univ, Dept CSE, Dhaka, Bangladesh
关键词
Big5 personality traits (BPT); data analysis; explainable AI; machine learning (ML); personality-based prediction; social media; software development trends; stack overflow (SO); Twitter (X); PERFORMANCE; QUESTIONS; ASK;
D O I
10.1002/spe.3381
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Many programming languages and technologies have appeared for the purpose of software development. When choosing a programming language, the developers' cognitive attributes, such as the Big5 personality traits (BPT), may play a role. The developers' personality traits can be reflected in their social media content (e.g., tweets, statuses, Q&A, reputation). In this article, we predict the developers' programming language preferences (i.e., the pattern of picking up a language) from their BPT derived from their content produced on social media. We randomly collected data from a total of 820 Twitter (currently X) and Stack Overflow (SO) users. Then, we collected user features (i.e., BPT, word embedding of tweets) from Twitter and programming preferences (i.e., programming tags, reputation, question, answer) from SO. We applied various machine learning (ML) and deep learning (DL) techniques to predict their programming language preferences from their BPT. We also investigated other interesting insights, such as how reputation and question-asking/replying are associated with the users' BPT. The findings suggest that developers with high openness, conscientiousness, and extraversion are inclined to mobile applications, object-oriented programming, and web programming, respectively. Furthermore, developers with high openness and conscientiousness traits have a high reputation in the SO community. Our ML and DL techniques classify the developers' programming language preferences using their BPT with an average accuracy of 78%.
引用
收藏
页码:473 / 490
页数:18
相关论文
共 119 条
  • [1] Abas A R., 2022, Computers, Materials Continua, V71
  • [2] Ahmed F., 2015, SOFT SKILLS SOFTWARE
  • [3] Akimov O., 2021, J ENTREPRENEURSHIP, V25, P1
  • [4] How Popularity Shapes User Interactions in Tech-Related Online Communities
    Al Rubaye, Abduljaleel
    Sukthankar, Gita
    [J]. PROCEEDINGS OF THE 2023 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING, ASONAM 2023, 2023, : 310 - 314
  • [5] Personality traits, individual innovativeness and satisfaction with life
    Ali, Imran
    [J]. JOURNAL OF INNOVATION & KNOWLEDGE, 2019, 4 (01): : 38 - 46
  • [6] Semantic-k-NN algorithm: An enhanced version of traditional k-NN algorithm
    Ali, Munwar
    Jung, Low Tang
    Abdel-Aty, Abdel-Haleem
    Abubakar, Mustapha Y.
    Elhoseny, Mohamed
    Ali, Irfan
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2020, 151
  • [7] The impact of personality traits and knowledge collection behavior on programmer creativity
    Amin, Aamir
    Basri, Shuib
    Rahman, Mobashar
    Capretz, Luiz Fernando
    Akbar, Rehan
    Gilal, Abdul Rehman
    Shabbir, Muhammad Farooq
    [J]. INFORMATION AND SOFTWARE TECHNOLOGY, 2020, 128
  • [8] Predicting Personality from Book Preferences with User-Generated Content Labels
    Annalyn, Ng
    Bos, Maarten W.
    Sigel, Leonid
    Li, Boyang
    [J]. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2020, 11 (03) : 482 - 492
  • [9] Antu B. N., 2024, PREDICTING PROGRAMMI
  • [10] Arnoux P. H., 2017, 11 INT AAAI C WEB SO, V11, P472