Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 480-487.

• Software Engineering & Database Technology • Previous Articles     Next Articles

Software Stage Effort Prediction Based on Analogy and Grey Model

WANG Yong1, LI Yi1, WANG Li-li1, ZHU Xiao-yan2   

  1. School of Information Science and Engineering,Ocean University of China,Qingdao 266100,China1
    School of Electronics and Information Engineering,Xi’an Jiaotong University,Xi’an 710049,China2
  • Online:2019-02-26 Published:2019-02-26

Abstract: Accurate software effort prediction is one of the most challenging tasks in the software engineering domain.Due to the inherent uncertainty and risk of software development process,it is insufficient to predict the whole effort just at the early stage of the project.In contrast,it is important to predict the effort of each stage during the software development process.This enables the managers to reallocate resources according to the variation of the project development and ensures the project to be completed with the prescribed schedule and under the budget.Therefore,this paper presented a new method for software physical time stage-effort prediction based on both analogy method and grey mo-del.The proposed hybrid method obtains prediction results by combining the values predicted by both analogy and grey model.At the same time,this method can avoid the limitations of using either of them.The experimental results on real world software engineering dataset indicate that the prediction accuracy obtained by the proposed method is better than that obtained by analogy method,GM (1,1) model,GV,Kalman filter and linear regression,showing great potential.

Key words: Analogy, Grey model, Stage effort prediction, Software project management

CLC Number: 

  • TP311
[1]JORGENSEN M,SHEPPERD M.A systematic review of software development cost estimation studies[J].IEEE Transactions on Software Engineering,2006,33(1):33-53.
[2]WANG Y,SONG Q B,SHEN J Y.Grey learning based software stage-effort estimation[C]∥2007 International Conference on Machine Learning and Cybernetics.IEEE,2007:1470-1475.
[3]HASTIE S,WOJEWODA S.Standish Group 2015 Chaos Report Q&A with Jennifer Lynch[J].Retrieved,2015,1(15):2016.
[4]BOEHM B W.Understanding and Controlling Software Costs [J].IEEE Transactions on Software Engineering,1988,14(10):1462-1477.
[5]贾经冬,林广艳.软件项目管理[M].北京:高等教育出版社,2012.
[6]AZZEH M,NASSIF A B.A hybrid model for estmating soft-ware project effort from Use Case Points[J].Applied Soft Computing,2016,49:981-989.
[7]CHOU J S,WU C C.Estimating software project effort for manufacturing firms[J].Computers in Industry,2013,64(6):732-740.
[8]MOLOKKEN K,JORGENSEN M.A review of software sur-veys on software effort estimation[C]∥2003 International Symposium on Empirical Software Engineering,2003(ISESE 2003).IEEE,2003:223-230.
[9]HUGHES R T.Expert judgement as an estimating method[J].Information and Software Technology,1996,38(2):67-75.
[10]SRINIVASAN K,FISHER D.Machine learningapproaches to estimating software development effort[J].IEEE Transactions on Software Engineering,1995,21(2):126-137.
[11]SONG Q B,SHEPPERD M,MAIR C.Using Grey Relational Analysis to Predict Software Effort with Small Data Sets[C]∥IEEE International Symposium on Software Metrics.IEEE,2005:35.
[12]HSU C J,HUANG C Y.Comparison of weighted grey relational analysis for software effort estimation[J].Software Quality Journal,2011,19(1):165-200.
[13]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.
[14]SHEPPERD M,SCHOFIELD C.Estimating software project effort using analogies[J].IEEE Transactions on Software Engineering,1997,23(11):736-743.
[15]MUKHOPADHYAY T,VICINANZA S S,PRIETULA M J.Examining the feasibility of a case-based reasoning model for software effort estimation[J].MIS quarterly,1992,16(2):155-171.
[16]AZZEH M,COWLING P I,NEAGU D.Software stage-effort estimation based on association rule mining and Fuzzy set theory[C]∥2010 IEEE 10th International Conference on Computer and Information Technology (CIT).IEEE,2010:249-256.
[17]BOEHM B W.Software engineering economics[M].Englewood Cliffs (NJ):Prenticehall,1981.
[18]DENG J L.Control problems of grey systems[J].Systems & Control Letters,1982,1(5):288-294.
[19]MONTGOMERY D C,PECK E A,VINING G G.Introduction to linear regression analysis [M].John Wiley & Sons,2015.
[20]HEIAT A.Comparison of artificial neural netwo-rk and regression models for estimating software development effort[J].Information and Software Technology,2002,44(15):911-922.
[21]HUANG H,HUANG S,CHEN J,et al.An image information hiding algorithm based on grey system theory[J].International Journal of Communication Systems,2014,27(10):2426-2442.
[22]SU S L,SU Y C,HUANG J F.Grey-based power control for DS-CDMA cellular mobile systems[J].IEEE Transactions on Vehicular Technology,2000,49(6):2081-2088.
[23]GUO J J,WU J Y,WANG R Z.A new approach to energy consumption prediction of domestic heat pump water heater based on grey system theory[J].Energy and Buildings,2011,43(6):1273-1279.
[24]KEUNG J.Software development cost estimation using analogy:a review[C]∥2009 Australian Software Engineering Conference.IEEE,2009:327-336.
[25]AZZEH M,NEAGU D,COWLING P I.Analogy-based software effort estimation using Fuzzy numbers[J].Journal of Systems and Software,2011,84(2):270-284.
[26]WANG Y,SONG Q B,MACDONELL S,et al.Integrate the GM (1,1) and Verhulst models to predict software stage effort[J].IEEE Transactions on Systems,Man,and Cybernetics,Part C (Applications and Reviews),2009,39(6):647-658.
[27]YANG Y,HE M,LI M,et al.Phase distribution of software development effort[C]∥Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement.ACM,2008:61-69.
[28]MACDONELL S G,SHEPPERD M J.Using priorphase effort records for reestimation during so-ftware projects[C]∥Ninth International Software Metrics Symposium.IEEE,2003:73-86.
[29]KULKARNI A,GREENSPAN J B,KRIEGMAN D A,et al.A generic technique for developing a software sizing and effort estimation model[C]∥Twelfth International Conference onComputer Software and Applications,1988(COMPSAC88).IEEE,1988:155-161.
[30]OHLSSON M C,WOHLIN C.An empirical study of effort estimation during project execution[C]∥Sixth International Software Metrics Symposium.IEEE,1999:91-98.
[31]TSUNODA M,TODA K,FUSHIDA K,et al.Revisiting software development effort estimation based on early phase deve-lopment activities[C]∥Mining Software Repositories.IEEE,2013:429-438.
[32]FERRUCCI F,GRAVINO C,SARRO F.Exploiting prior-phase effort data to estimate the effort for the subsequent phases:a further assessment[C]∥Proceedings of the 10th International Conference on Predictive Models in Software Engineering.ACM,2014:42-51.
[33]AZZEH M,ELSHEIKH Y,ALSEID M.An Optimized Analogy-Based Project Effort Estimation[J].International Journal of Advanced Computer Science & Applications,2014,5(4):6-11.
[34]SIGWENI B,SHEPPERD M.Feature weighting techniques for CBR in software effort estimation studies:a review and empirical evaluation[C]∥Proceedings of the 10th International Confe-rence on Predictive Models in Software Engineering.ACM,2014:32-41.
[35]KOLODNER J.Case-based reasoning[M].Morgan Kaufmann,2014.
[36]AHA D W.Case-based learning algorithms[C]∥Proceedings of the 1991 DARPA Case-Based Reasoning Workshop.1991:147-158.
[37]刘思峰,杨英杰,吴立丰.灰色系统理论及应用(第7版)[M].北京:科学出版社,2014.
[38]崔立志,刘思峰.基于数据变换技术的灰色预测模型[J].系统工程,2010(5):104-107.
[39]CHEN Z,MENZIES T,PORT D,et al.Finding the right data for software cost modeling[J].IEEE Software,2005,22(6):38-46.
[40]LITTLE R J A,RUBIN D B.Statistical analysis with missing data[M].John Wiley & Sons,2014.
[41]STRIKE K,EL EMAM K,MADHAVJI N.Software cost estimation with incomplete data[J].IEEE Transactions on Software Engineering,2001,27(10):890-908.
[42]SHEPPERD M,MACDONELL S.Evaluating prediction sys-tems in software project estimation[J].Information and Software Technology,2012,54(8):820-827.
[43]WHIGHAM P A,OWEN C A,MACDONELL S G.A baseline model for software effort estimation[J].ACM Transactions on Software Engineering and Methodology(TOSEM),2015,24(3):20.
[44]MITTAS N,MAMALIKIDIS I,ANGELIS L.A framework for comparing multiple cost estimation methods using an automated visualization toolkit[J].Information and Software Technology,2015,57:310-328.
[45]KITCHENHAM B,MADEYSKI L,BUDGEN D,et al.Robust statistical methods for empirical software engineering[J].Empirical Software Engineering,2018,22(2):579-630.
[46]KOHAVI R.A study of cross-validation and boot-strap for accuracy estimation and model selection[C]∥IJCAI.1995:1137-1145.
[47]KALMAN R E.A New Approach to Linear Filtering and Prediction Problems[J].Journal of Basic Engineering Transactions,1960,82:35-45.
[48]KITCHENHAM B A,PICKARD L M,MACDONELL S G,et al.What accuracy statistics really measure[J].IEE Proceedings-Software,2001,148(3):81-85.
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[9] . [J]. Computer Science, 2009, 36(5): 1-6.
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