Metric-based method of software requirements correctness improvement

被引:1
|
作者
Yaremchuk, Svitlana [1 ]
Bardis, Nikolaos [2 ]
Vyacheslav, Kharchenko [3 ]
机构
[1] Natl Univ Odessa Maritime Acad, Danube Inst, UA-68600 Odessa, Ukraine
[2] Hellen Mil Acad, Dept Math & Engn Sci, Athens 16673, Greece
[3] Natl Space Univ, Dept Comp Syst & Networks, UA-61070 Kiev, Ukraine
关键词
D O I
10.1051/itmconf/20170903009
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The work highlights the most important principles of software reliability management (SRM). The SRM concept construes a basis for developing a method of requirements correctness improvement. The method assumes that complicated requirements contain more actual and potential design faults/defects. The method applies a newer metric to evaluate the requirements complexity and double sorting technique evaluating the priority and complexity of a particular requirement. The method enables to improve requirements correctness due to identification of a higher number of defects with restricted resources. Practical application of the proposed method in the course of demands review assured a sensible technical and economic effect.
引用
收藏
页数:8
相关论文
共 50 条
  • [31] Sparse metric-based mesh saliency
    Hu, Shanfeng
    Liang, Xiaohui
    Shum, Hubert P. H.
    Li, Frederick W. B.
    Aslam, Nauman
    [J]. NEUROCOMPUTING, 2020, 400 : 11 - 23
  • [32] Near Data Processing Performance Improvement Prediction via Metric-Based Workload Classification
    Papalekas, Dimitrios
    Tziouvaras, Athanasios
    Floros, George
    [J]. 2022 11TH INTERNATIONAL CONFERENCE ON MODERN CIRCUITS AND SYSTEMS TECHNOLOGIES (MOCAST), 2022,
  • [33] Do we have to rely on metric-based quality improvement strategies for the management of ESKD?
    Pizzarelli, Francesco
    Basile, Carlo
    [J]. NEPHROLOGY DIALYSIS TRANSPLANTATION, 2022, 37 (03) : 397 - 399
  • [34] A distance metric-based space-filling subsampling method for nonparametric models
    Diao, Huaimi
    Wang, Dianpeng
    He, Xu
    [J]. ELECTRONIC JOURNAL OF STATISTICS, 2024, 18 (02): : 3247 - 3273
  • [35] Variational Metric Scaling for Metric-Based Meta-Learning
    Chen, Jiaxin
    Zhan, Li-Ming
    Wu, Xiao-Ming
    Chung, Fu-lai
    [J]. THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 3478 - 3485
  • [36] Metric-Based Semi-Supervised Regression
    Liu, Chien-Liang
    Chen, Qing-Hong
    [J]. IEEE ACCESS, 2020, 8 : 30001 - 30011
  • [37] Improved Metric-Based Recommender by Historical Interactions
    Jiang, Yubo
    Zhu, Yunfang
    Du, Xin
    Jin, Tao
    [J]. IEEE ACCESS, 2019, 7 : 125969 - 125975
  • [38] Kidney Biopsy Adequacy A Metric-based Study
    Ferrer, German
    Andeen, Nicole K.
    Lockridge, Joseph
    Norman, Douglas
    Foster, Bryan R.
    Houghton, Donald C.
    Troxell, Megan L.
    [J]. AMERICAN JOURNAL OF SURGICAL PATHOLOGY, 2019, 43 (01) : 84 - 92
  • [39] A Verification Method of the Correctness of Requirements Ontology
    Huy, Bui Quang
    Ohnishi, Atsushi
    [J]. KNOWLEDGE-BASED SOFTWARE ENGINEERING, 2012, 240 : 1 - 10
  • [40] On the impact of service-oriented patterns on software evolvability: a controlled experiment and metric-based analysis
    Bogner, Justus
    Wagner, Stefan
    Zimmermann, Alfred
    [J]. PEERJ COMPUTER SCIENCE, 2019, 2019 (08)