基于广义模糊数的软件成本加权CBR估算研究

摘要/Abstract

摘要： 在软件项目开发过程中,准确估算出软件成本在提高软件质量和保障软件成功开发方面起到重要支撑作用。针对软件项目历史数据库中部分属性在项目开发初期难以给予精确数值(仅仅能给出模糊数),而已有软件成本估算模型不能很好地处理模糊信息的问题,本文在基于案例推理模型(CBR)基础上集成广义模糊数,提出了基于广义模糊数的CBR模型。使用基于广义模糊数的相似度度量方法代替传统CBR模型中采用的欧式距离等相似度度量方法,采用模糊C均值聚类(FCM)方法将已有软件项目历史数据库中的精确数值进行模糊化处理,以匹配新项目中的模糊数。进一步采用粒子群算法(PSO)来优化属性的权重,构建基于广义模糊数的加权CBR模型。最终在实验中采用Desharnais数据来检验构建模型的有效性。实证结果表明,在与常用的欧式距离CBR模型相比,构建的基于广义模糊数的加权CBR模型能有效提高估算精度,采用PSO优化属性权重能提高模型的估算精度。

关键词: 软件成本估算, 基于案例推理, 广义模糊数, 权重优化

Abstract: In the software development process, accurate estimation of software effort is of great significance for software project and the enterprise. In order to overcome the difficulties that there isn't an accurate number for new software project at early stages according to the attributes of history project dataset and the existing software effort estimation models can't deal with the fuzzy number effectively, case-based reasoning and generalized fuzzy number are integrated, and a case-based reasoning (CBR) model based on generalized fuzzy number is proposed for software effort estimation in this paper. The traditional similarity measure such as Euclidean distance is replaced by a new similarity measure based on generalized fuzzy number in CBR model. Furthermore, the fuzzy c-means clustering is applied to fuzz the accurate number in history project dataset. Moreover, particle swarm optimization (PSO) is employed to further optimize attribute weights of the model. Finally Desharnais data is adopted to examine the validity of the model. It is shown that the proposed generalized fuzzy numbers CBR model can improve the estimation accuracy in comparison with the commonly used Euclidean distance CBR. In addition, it is also shown that the model with optimized weight from PSO can improve the estimation accuracy.

Key words: software effort estimation, case based reasoning, generalized fuzzy number, weight optimization

中图分类号:

TP311.5

吴登生. 基于广义模糊数的软件成本加权CBR估算研究[J]. 中国管理科学, 2013, 21(4): 178-186.

Wu Deng-sheng. Case-based Reasoning with Optimized Weight for Software Cost Estimation Based on Generalized Fuzzy Number[J]. Chinese Journal of Management Science, 2013, 21(4): 178-186.

参考文献

[1] Lackman M. Controlling the project development cycle[J]. IEEE Engineering Management Review, 1987, 15(3): 56-78.
[2] Molokken-Ostvold K, Haugen N C, Benestad H C. Using planning poker for combining expert estimates in software projects[J]. Journal of Systems and Software, 2008, 81(12): 2106-2117.
[3] Koch S, Mitlhner J. Software project effort estimation with voting rules[J]. Decision Support Systems, 2009, 46(4): 895-901.
[4] Boehm B W, Valerdi R. Achievements and challenges in cocomo-based software resource estimation[J]. IEEE Software, 2008, 25(5): 74-83.
[5] Mittas N, Athanasiades M, Angelis L. Improving analogy-based software cost estimation by a resampling method[J]. Information and Software Technology, 2008, 50(3): 221-230.
[6] Vinay Kumar K, Ravi V, Carr M, et al. Software development cost estimation using wavelet neural networks[J]. Journal of Systems and Software, 2008, 81(11): 1853-1867.
[7] Oliveira A L I. Estimation of software project effort with support vector regression[J]. Neurocomputing, 2006, 69(13-15): 1749-1753.
[8] Sentas P, Angelis L, Stamelos I, et al. Software productivity and effort prediction with ordinal regression[J]. Information and Software Technology, 2005, 47(1): 17-29.
[9] Elish M O. Improved estimation of software project effort using multiple additive regression trees[J]. Expert Systems with Applications, 2009, 36(7): 10774-10778.
[10] Cheikhi, L, Abran, A, Desharnais, LM. Analysis of the ISBSG software repository from the ISO 9126 view of software product quality[C]. 38th Annual Conference on IEEE-Industrial-Electronics-Society, Montreal,Oct,25-28,2012.
[11] Maxwell K D. Applied statistics for software managers[M]. New Jersey: Prentice Hall, 2002.
[12] Wu Dengsheng, Li Jianping, Liang Yong. Linear combination of multiple case-based reasoning with optimized weight for software effort estimation[J]. Journal of Supercomputing, 2013, 64(3): 898-918.
[13] Nassif A B, Ho D, Capretza L F. Towards an early software estimation using log-linear regression and a multilayer perceptron model[J]. Journal of Systems and Software, 2013, 86(1): 44-160.
[14] Azzeh M, Neagu D, Cowling P I. Analogy-based software effort estimation using fuzzy numbers[J]. The Journal of Systems and Software, 2011, 84 (2): 270-284.
[15] Heemstra F J. Software cost estimation[J]. Information and Software Technology, 1992, 34(10): 627-639.
[16] Mittas N, Angelis L. Ranking and clustering software cost estimation models through a multiple comparisons algorithm[J]. IEEE Transactions on Software Engineering, 2013, 39(4): 537-551.
[17] Dejaeger K, Verbeke W, Martens D, Baesens B. Data Mining techniques for software effort estimation: A comparative study[J]. IEEE Transactions on Software Engineering, 2012, 38(2): 375-397.
[18] Shepperd M, Schofield C. Estimating software project effort using analogies[J]. IEEE Transactions on Software Engineering, 1997, 23 (12): 736-743.
[19] Li Y F, Xie M, Goh T N. A study of project selection and feature weighting for analogy based software cost estimation[J]. Journal of Systems and Software, 2009, 82(2): 241-252.
[20] Chiu N H, Huang S J. The adjusted analogy-based software effort estimation based on similarity distances[J]. Journal of Systems and Software, 2007, 80(4): 628-640.
[21] Huang S J, Chiu N H, Chen L W. Integration of the grey relational analysis with genetic algorithm for software effort estimation[J]. European Journal of Operational Research, 2008, 188(3): 898-909.
[22] Azzeh M, Neagu D, Cowling P I. Fuzzy grey relational analysis for software effort estimation[J]. Empirical Software Engineering, 2010, 15(1): 60-90.
[23] 吴登生,李建平,蔡晨.软件成本估算的粒子群算法类比模型及自助法推断[J].管理科学,2010,23(3):113-120.
[24] 梁昌勇,顾东晓,范昕,陈文恩.面向不确定多属性决策问题的范例检索算法研究[J].中国管理科学,2009,17(1):131-137.
[25] 路云,吴应宇,达庆利.基于案例推理技术的企业经营决策支持模型设计[J].中国管理科学,2005,13(2):81-87.
[26] Chen S J, Chen S M. Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers[J]. IEEE Transactions on Fuzzy Systems, 2003, 11(1): 45-56.
[27] 文成林,周哲,徐晓滨.一种新的广义梯形模糊数相似性度量方法及在故障诊断中的应用[J].电子学报,2011,39(3A):1-6.
[28] Wei S H, Chen S M. A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers[J]. Expert Systems with Applications, 2009, 36(1): 589-598.
[29] 张增刚,郑贤斌,李继志.基于广义模糊数相似测度风险分析方法[J].系统工程理论与实践,2010,30(4):738-743.
[30] Xie X L, Beni G. A validity measure for fuzzy clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(8): 841-847.
[31] 张俊光,杨芳芳,杨双.基于重大偏差标准的软件项目工作量管理方法研究[J].中国管理科学,2013,21(2):161-167.