Finding the Effectiveness of Software Team Members Using Decision Tree

被引:4
|
作者
Omar, Mazni [1 ]
Syed-Abdullah, Sharifah-Lailee [2 ]
机构
[1] UUM, Sch Comp, Sintok 06010, Kedah, Malaysia
[2] Univ Teknol MARA, Fac Comp & Math Sci, Dept Comp Sci, Arau 02600, Perlis, Malaysia
关键词
Software team performance; Decision tree; Software methodology; Personality types; Personality diversity; Academic achievement;
D O I
10.1007/978-3-319-17398-6_10
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents steps taken in finding the effectiveness of software team members using decision tree technique. Data sets from software engineering (SE) students were collected to establish pattern relationship among four predictor variables-prior academic achievements, personality types, team personality diversity, and software methodology-as input to determine team effectiveness outcome. There are three main stages involved in this study, which are data collection, data mining using decision tree, and evaluation stage. The results indicate that the decision tree technique is able to predict 69.17 % accuracy. This revealed that the four predictor variables are significant and thus should consider in building a team performance prediction model. Future research will be carried to obtain more data and use a hybrid algorithm to improve the model accuracy. The model could facilitate the educators in developing strategic planning methods in order to improve current curriculum in SE education.
引用
收藏
页码:107 / 115
页数:9
相关论文
共 50 条
  • [21] Building decision tree software quality classification models using genetic programming
    Liu, Y
    Khoshgoftaar, TM
    GENETIC AND EVOLUTIONARY COMPUTATION - GECCO 2003, PT II, PROCEEDINGS, 2003, 2724 : 1808 - 1809
  • [22] Towards an automated classification phase in the software maintenance process using decision tree
    Alturki, Sahar
    Almoaiqel, Sarah
    PEERJ COMPUTER SCIENCE, 2024, 10 : 1 - 16
  • [23] Using Artificial Team Members for Team Training in Virtual Environments
    van Diggelen, Jurriaan
    Muller, Tijmen
    van den Bosch, Karel
    INTELLIGENT VIRTUAL AGENTS, IVA 2010, 2010, 6356 : 28 - 34
  • [24] A proposed framework for effective software team performance: A mapping study between the team members' personality and team climate
    Shameem, Mohammad
    Kumar, Chiranjeev
    Chandra, Bibhas
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA), 2017, : 912 - 917
  • [25] Comparison of decision tree methods for finding active objects
    Zhao, Yongheng
    Zhang, Yanxia
    ADVANCES IN SPACE RESEARCH, 2008, 41 (12) : 1955 - 1959
  • [26] TeamPlus: A Decision Support System for Software Team Formation
    Cunha, Felipe
    Perkusich, Mirko
    Almeida, Hyggo
    Gorgonio, Kyller
    Perkusich, Angelo
    36TH BRAZILIAN SYMPOSIUM ON SOFTWARE ENGINEERING, SBES 2022, 2022, : 118 - 123
  • [27] Quantifying effectiveness of team recommendation for collaborative software development
    Noppadol Assavakamhaenghan
    Waralee Tanaphantaruk
    Ponlakit Suwanworaboon
    Morakot Choetkiertikul
    Suppawong Tuarob
    Automated Software Engineering, 2022, 29
  • [28] Quantifying effectiveness of team recommendation for collaborative software development
    Assavakamhaenghan, Noppadol
    Tanaphantaruk, Waralee
    Suwanworaboon, Ponlakit
    Choetkiertikul, Morakot
    Tuarob, Suppawong
    AUTOMATED SOFTWARE ENGINEERING, 2022, 29 (02)
  • [29] Soft Skills in Software Development Teams A Survey of the Points of View of Team Leaders and Team Members
    Matturro, Gerardo
    Raschetti, Florencia
    Fontan, Carina
    2015 IEEE/ACM 8TH INTERNATIONAL WORKSHOP ON COOPERATIVE AND HUMAN ASPECTS OF SOFTWARE ENGINEERING CHASE 2015, 2015, : 101 - 104
  • [30] Exploring decision making style as a predictor of team effectiveness
    Verma, Neha
    Rangnekar, Santosh N.
    Barua, Mukesh Kumar
    INTERNATIONAL JOURNAL OF ORGANIZATIONAL ANALYSIS, 2016, 24 (01) : 36 - 63