Fuzzy structural dependency constraints in software release planning

被引:0
作者
An, NT [1 ]
Saliu, MO [1 ]
机构
[1] Univ Calgary, Dept Comp Sci, Calgary, AB T2N 1N4, Canada
来源
FUZZ-IEEE 2005: PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS: BIGGEST LITTLE CONFERENCE IN THE WORLD | 2005年
关键词
incremental software development; release planning; uncertainty; fuzzy logic;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Incremental software development is becoming an important tendency in software engineering. A software release is a collection of new and/or changed features that form a new product. Release planning for incremental software development assigns features to sequence of releases in the most beneficial way within the resources available. Release planning is a complex problem where most of the data available are usually uncertain. In this paper, we propose an approach that improves on existing methods for release planning by handling the uncertainty of data using fuzzy logic. We use fuzzy logic to model the uncertainty concerning the identification of structural dependency constraints between requirements. All the concepts and the complete approach are illustrated by a case study example.
引用
收藏
页码:442 / 447
页数:6
相关论文
共 18 条
  • [1] Adaptive fuzzy logic-based framework for software development effort prediction
    Ahmed, MA
    Saliu, MO
    AlGhamdi, J
    [J]. INFORMATION AND SOFTWARE TECHNOLOGY, 2005, 47 (01) : 31 - 48
  • [2] [Anonymous], 7 INT C FUZZ THEOR T
  • [3] Release planning in market-driven software product development: Provoking an understanding
    Carlshamre P.
    [J]. Requirements Engineering, 2002, 7 (3) : 139 - 151
  • [4] Carlshamre P, 2001, FIFTH IEEE INTERNATIONAL SYMPOSIUM ON REQUIREMENTS ENGINEERING, PROCEEDINGS, P84
  • [5] Dubois D., 1980, FUZZY SET SYST
  • [6] Gemoets L., 1994, NAFIPS/IFIS/NASA '94. Proceedings of the First International Joint Conference of the North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic (Cat. No.94TH8006), P182, DOI 10.1109/IJCF.1994.375102
  • [7] KHOSHGOFTAAR, 2000, IEEE INT S HIGH ASS, P281
  • [8] Selecting engineering techniques using fuzzy logic based decision support
    Liggesmeyer, P
    [J]. IEEE SYMPOSIUM AND WORKSHOP ON ENGINEERING OF COMPUTER-BASED SYSTEMS, PROCEEDINGS, 1996, : 427 - 434
  • [9] FULSOME: A fuzzy logic modeling tool for software metricians
    MacDonell, SG
    Gray, AR
    Calvert, JM
    [J]. 18TH INTERNATIONAL CONFERENCE OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY - NAFIPS, 1999, : 263 - 267
  • [10] Musilek P., 2000, Applied Computing Review, V8, P24, DOI 10.1145/373975.373984