SEC journal No.16 - IPA 独立行政法人 情報処理推進機構
Contents
1. Kirkpatrick 20 0000000003 20070 00 5 O 1 NEXCESSU SEC journal Vol 5 2009 35 0 3000000000000 2 1 551 00000 20040 000000000000000000 1 00000000000000000000000000 2 550 00000000000 00000000000000006000000000 Ul 0000000000000000000 SECO 552. 0000 00009001 H HELL 0000 20000000000000000000 03 Daun 0000 000000000000000000000 00000000000000000000 00000000 00000000 00000000000 0000000000000000 0000 0000000000000 0000060 00000000000000000000000000 000000000000000000 00000000000000000000000 00000000000000000000000
3. EPM I DI HL DEL B HEEL BL O FH DE UO UO E BE 7 HE HU U LE EI UI 0 3 000000004 e ISEMA Philip Johnson O O U O U U ISESE METRICS 0000 10 00000 ESEM2007 0 2007 0 900 0000 000 000 000 0 0 0 0 01 0
4. 52 111 11 1 SEC journal Vol 5 No 1 2009 53 0 300 000000000000000 D UBL 53 00000 0000 0 0 0 0 00000 00 BE E EEE E BE EB EEE ERDE BER
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7. 0 000 SEC journal Vol 5 No 1 2009 5 Publish or peris I DU D BD BD B O l 0 Ul
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20. 0000 SEC2008 IPA SECT 00000 000 000 000 000 0000 0 2008 SEC journal Vol 5 No 1 2009 3 REVIEW OU Ld OUU TU Mary Poppendieck Tom Poppendieck ISBN 978 4 8222 8350 6 35200010 2 5200 00000 20080 2r Implementing Lean Software Development From Concept to Cash UBI uult t 000 15 978 4 502 39430 0 5 2400000 2 6250 00000 20070 700
21. SEC journal Vol 5 No 1 2009 00000000000 0000000000 Next Generation Network E E E E E E E E E E E EL ET LI 0 0 lt 00000000000000000000000000 0000000000000 00000 000 SEC 0 4000000000000 00000 0 00000 00 0060 00000 00 0 000000000000000000 0 0 000000000000000000 00000
22. 00000 ERE EREE AUE E a e e DRE EE RL dg dL E EET EISE 8 uunc SE EEE uui U HESSE ZE LKOWITZ1998 M Zelkowitz and D Wallance Models for Validating Technology IEEE Computer Vol 31
23. 21000000000 2 13 55 00 0 550 0 0 55 00 000000000 00000000 SEC2007 IPA SEC O OOOO OO O 20070 0000000 2007 1 0000000000000 55 L UL EISS ILI l SECI l 5 1
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50. 2 D pp duddgutuuiitutub t IUUUUUUUUUUUUUUUUUUUUUUUU U IUUUUUUUUUUUUUUUUUUUUUUUU U IUUUUUUUUUUUUUUUUUUUUUUU UU ELLE DE DEE DEDE 000000000 MTANI2007 00000 EPM IUUUUUUUUUUUUUUUUUUUUUUU UU ESEM2007 IUUUUUUUUUUUUUUUUUUUUUUU U U IUUUUUUUUUUUUUUUUUUUUUUU UU 0000 0000 06 OOL E x 24 Unified Modding Language SEC journal Vol 5 No 1 Mar200 43 BEBE DELETE ET DE EE BIET E Bo DEDE BE BE E E BLUE HE B 11111 StagE
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106. 00 0000000000000000000000 00 0 00 000000000000000000000 J EFFERY2000 MAXWELL 1999 ME NDE 5200411 O O O uumuguuiiuuuiluuiiluliiuttututtLtut uguguauululiutuimuuiiitututututLlu tt 0000000000000000000000000 0000000000000000000000000 00000000000000000000000000 00 0 0 0000 0 00 BLACK BURN 1996 Blackburn Scudder L Wassenhove Improving speed and productivity of software development global survey of software developers IEEE Trans on Software E ng Vol 22 No 12 pp 875 885 1996 BOEHM 1981 B W Boehm Software Engineering Economics Prentice Hall New Jersey 1981 BRIAND2000 L Briand T Langley
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109. SEE HEB EFE BS REEE E E EE BEHEH 40 5 CEE CEOE EE BSE EEEFER EEE SE BE IE HEE HESABE Ag AE ASE HERE REL Tate ERA Pd ELE EIE ela ele Ls SEC EHO PETS ET ERES RS RS LT H E HE E 19661 00 0000000000000000000000 00000000000000000000020 190000000 00000000 00000000000 000000000000 0 020090 1000 O DD UO D SEC journal Vol 5 1 2009 09 Overseas Report NASSCOM Quality Summit 2008 OUUU L 5 00 ut 5 0 0000 20080 100 150 0000 1600000000000 NASSCOM Quality Summit 20088 0000000000000000000000 000000000000000000000000 000000000000000000000000 0000000000000000000000 1 55 Quality 1 0 0 1 U U 110000000000000000000000 Subroto Bagchi
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111. Wieczorrek A replicated assessment and comparison of common software cost modeling techniques Proc 22nd IEEE International Conference on Software Eng Limerick pp 377 386 2000 CONTE 1986 S Conte H Dunsmore and V Shen Software Engineering Metrics and Models Benjamin Cummings MenloPark California 1986 HASTIE 2001 T Hastie R Tibshirani and Friedman The Elements of Statistical Learning Data Mining Inference and Prediction Springer Verlag New York 2001 HOMMA2004 0 0 000 5 0000000000000 000000000 0010000000000 53 610 2004 7 EFFERY1996 effery Time sensitive cost models in commercial MIS environment IEEE Trans on Software Eng Vol 13 No 7 pp 852 859 1996 J EFFERY2000 Jeffery M Ruhe and I Wieczorek A comparative study of two software development cost modeling techniques using multi organizational and company specific data Information and Software Technology Vol 42 No 14 pp 1009 1016 2000 KITCHENHAM2001 B Kitchenham L Pickard and S MacDonell What accuracy statistics really measure IEE Proceedings Software Vol 148 Issue 3 pp 81 85 2001 MAXWELL 1996 Maxwell L Wassenhove and S Dutta Software development productivity of European space military and industrial applications IEEE Transion Software Eng Vol 22 No 10 pp 706 718 1996 MAXWELL 1999 K Maxwell L Wassenhove S Dutta Performance evaluation of general an
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128. No 5 pp 23 31 May 1998 2008 00 00000 00 00 00000 0 00 0 000 00000 1500 00000000000000000000000000 2008 11 00000000100000000000000000000000000 000000000000000 tn http Wwww eice org jpn shiori iss 4 htmlz4 1 1994 00000000000000 00000000000 000 00000 810131 0000 1994 20081 00000 00000 000 00000 00 000 0 0 0 00 0000 0000000000000000000000 2008 6 SEC journal Vol 5 No 1 2009 5 000 0000 0 OO ttt 53000 000 0 00 0 00 0 43000 0 000 000 0 000 000 0 00 00 000 38 00 0 0 00 0 00000 0 000 000 0 0 00 0 000 0 0000000000000000000000000000000 3000000000000000 0 0000000000000000000 3000000000000000000000000000 50 000 000 000 00 0 00 0000 56 6 0 35 9 00000000000 Software Development Effort Estimation odels Stratified by Productivity F actors Masateru Tsunodaf Akito MondenT Ken ichi MatsumotoT Akihiko TakahashiTT TT T When building effort estimation models by multiple regression analysis it is preferable to stratify software projects acco
129. 00000 2 20 55066 0000 000000000 0000 Standard Skills ITA MOSI WG qaq 2 ETSSDUDUHWG ES 0000 80 00000 9000808000600 forETS 551 55 0000000 0000000 00000 0000 00000000 y 00000000 00000 11 0100 0000000000000 55 ei 0000000 856 00 001 50 130000 0000000000 0000000000 00000000000000 U 0000000 5 0 lJ g 34 00000000 0000000000 000000 000000000 V 000 0000000 00000000 900000 00000 00 0 0 0000000 000000
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144. 5 2007 00000 2 ISESE Software Engnesing x 3 ApPicaionInteface 4 METRICS Irtanaionad Software M 5 5 ESEM Symposumon Software Enginesing and x6 15 In Process Software Engn ing Meesurarertand Ard yss 200 00000 0 0 0000000 000000000 0 5 0000 LU 0000000000000 500 0 EPMO O 6 Sense 0 0 6 Sense 5 T Bolzano Bozen 00 ECG T SUMS 1 Pittsburg Super computing Center IBM Hekystat 00000 200100000000 40000 000 00 1990 000000000000000 30 00000 I D J 000 00 6 Sense Hakystat Hakystat 6 Sense 00 0000000000000 0 E E OE OI D D U U U D 6 Sense Analytics
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149. Kou M Agustin Q Zhang A Kagawa and T Yamashita Practical automated process and product metric collection and analysis in a classroom setting Lessons learned from Hackystat UH International Symposium on Empirical Software ISESE 2004 Redondo Beach pp 136 144 2004 8 J OHNSON2007 Philip M J ohnson Requirement and Design Trade offs in Hackystat 75 00000 0 0 0000000 000000000 EPMI 0 In Process Software Engineering Measurement and Analysis System E SEM 2007 pp 81 90 Madrid Spain 2007 9 MITANI ESEM2007 Yoshiki Mitani Tomoko Matsumura Mike Barker Seishiro Tsuruho Katsuro Inoue Ken Ichi Matsumoto Proposal of a Complete Life Cycle In Process Measurement Model Based on Evaluation of an In Process Measurement Experiment Using a Standardized Requirement Definition Process International Symposium on Empirical Software Engineering and Measurement ESEM 2007 pp 11 20 Madrid Spain 2007 9 MITANI2004 Yoshiki Mitani Mike Barker Seishiro Tsuruho Koji Torii An Experimental Framework for J apanese Academic Industry Collaboration in Empirical Software Engineering Research International Symposium on E mpirical Software Engineering ISESE 2004 Vol 2 pp 35 36 Poster session Redondo Beach 2004 8 MITANI2007 Yoshiki Mitani Tomoko Matsumura Mike Barker Seishiro Tsuruho Katsuro Inoue Ken Ichi Matsumoto An Empirical Study of Process and Product M
150. PlaxDo ChedeAd Adion 0000000 empirical empirica 0 000000000000 practical 0 000000 LT 0000000 Empirical Software Engineering T O Empirical Economics I LH LH U U 0 0 U Symposium Empirical Measurement Empirical software engineering an international journal U UU U U 0 uH tL Software Engineering and 000 2 00000000000000000000000000 00000 2008 SEC journal Vol 5 No 1 Mar 2009 5 Ul 0000000000000000000000000000000000000000000000005
151. Summit 2008 74 BOOK REVIEW 1 JU 0 00 6 00 000 00 tt Uu 20080 0 20 00000000000 100000 4
152. UUUUUUUUUUUUUUUUUUUUUUUU 0 00000000000000000000 00 000 0000 00 0000 06 0000 00 0 000 00 sat 100000000000000000000000 0000 000 000 10000000000000000000000 2 000 Google Amazon 0 0 00000 0000 00 0000 00 000 000
153. d company specific models in software development effort estimation Management Science Vol 45 No 6 pp 787 803 1999 MENDE 52004 E Mendes and B Kitchenham Further comparison of cross company and within company effort estimation models for web applications Proc 10th International Software Metrics Symposium pp 348 357 2004 2007 0 000 000 000 00000 000 000 0000 0 000 0000 journal 0 10 48 53 2007 SHEPPERD1997 Shepperd and C Schofield Estimating software project effort using analogies E EE Trans on Software ng Vol 23 No 12 736 743 1997 SRINIVASAN 1995 K Srinivasan and D Fisher Machine learning approaches to estimating software development effort IEEE Trans on Software Eng Vol 21 No 2 pp 126 137 1995 1995 0 0 0 000 000 0 O Windows 0000000000 0000000000000 1995 TSUNODA2005 0 0 0 0000000000000000000000000 Vol 46 No 5 1156 1164 2005 WALSTON1977 Walston and Method of programming measurement and estimation IBM Systems J ournal Vol 16 No 1 pp 54 73 1977 WINER1991 B Winer D Brown and K Michels Statistical Principles in Experimental Design 3rd edition McGraw Hill New York 1991 SEC journal Vol 5 Mar2000 Z 4100000000000 09 030385 0220
154. e http www rallydev com company SEC 172007 IPA SEC 2007 ISEC2007 0 00000 00 0000 0000 000 00000 0000000000000000 2007 5 2007 SOFTPIT2007 http www soft pit de tiki index php page SoftPitWorkshop2007 SPAGOBI SpagoBI http www spagobi org ecm faces public guest StagE StagE Project http www stage project jp StagE NAIST2008 StagE Project NAIST Osaka Univ Software Tag Standard V1 0 Draft English Translation V0 7 2008 10 StagE 2008 1 0 20080 100 140 O http www stage project jp dl php 1200861 Vol 91 6 2008 pp 510 514 2008 2008 000000000000000000000000000000 00000000000000000000000000000000 Vol 100 No 2008 pp 11 16 2008 20080 00000 0 6 000000000000000000 000000000000000 2008 2 20080 000000000000000000000000000 EPM SEC journal 15 pp 48 53 2008 SEC journal Vol 5 2009 45 UL 10000000000000000 000000000 ESQRDIT UL 0 0
155. etrics based on In process Measurements of a Standardized Requirements Definition Phase LNCS4895 Software Process and Product Measurement IWSM Mensura2007 Revised Papers Selected Papers 46 59 2007 MITANI2008 Yoshiki Mitani Hiroshi Ohtaka Ryozo Nagaoka Hiroyuki Yoshikawa Seishiro Tsuruho A Proposal for Integration of In process Project Visualization and Keeping Post process Traceability MIE RUKA Method and Software Framework Workshop on Accountability and Traceability in Global Software Engineering ATGSE 2008 pp 11 12 Beijing China 2008 OHIRA ISESE 2004 Masao Ohira Reishi Yokomori Makoto Sakai K en ichi Matsumoto Katsuro Inoue Michael Barker Empirical Project Monitor A System for Managing Software Development Projects in Real Time International Symposium on Empirical Software Engineering ISESE 2004 Vol 2 pp 37 38 Poster session Redondo Beach CA 2004 8 OHIRA MSR2004 Masao Ohira Reishi Yokomori Makoto Sakai Ken ichi Matsumoto Katsuro Inoue Empirical Project Monitor A Tool for Mining Multiple Project Data International Workshop on Mining Software Repositories MSR 20041 pp 42 46 Edinbargh Scotland UK 2004 OHTAKA2008 Hiroshi Ohtaka Ryozo Nagaoka Visualization of IT Project MIERUKA The 4th International Project Management Conference ProMAC 2008 Anchorage USA 2008 PRO PRO Metrics http www prom case unibz it RALLY Rally Softwar
156. lab naist jp A T GSE 2008 CIOLKOWSK12007 M Ciolkowski Heidrich Munch Simon and M Radicke E valuating Software Project Control Centers in Industrial Environments ESEM 2007 pp 314 323 Madrid Spain 2007 9 CIOLK OWSK12008 M Ciolkowski Heidrich Simon M Radicke Empirical Results from Using Custom Made Software Project Control Centers in Industrial Environments ESEM 20080 pp 243 252 Kaiserslautern Germany 2008 9 CMMI CMMI Metrics Framework http sesar dti unimi it CM MI metrics COLOMBO2008 Alberto Colombo Ernesto Damiani Fulvio Frati Sergio Oltolina Karl Reed Gabriele Ruffatti The Use of a Meta Model to Support Multi Project Process Measurement APSE C 2008 Beijing pp 503 510 China 2008 12 DAMIANI2008 Ernesto Damiani Frati Fulvio Sergio Oltolina Gabriele Ruffatti Improving Software Process Accountability with Spago4Q ATGSE 2008 pp 13 14 Beijing China 2008 12 DUMKE2007 Reiner Dumke Christof Ebert Software Measurement E stablish Extract Evaluate E xecute Chapter 8 30 Springer Verlag 2007 9 EASE EASE 000000 http www empirical jp EPM EPM J 00 httpz sec ipa go jp tool epm html NTT 1 E E HEIDRICH2008 ens Heidrich Jurgen Munch Implementing Software Project Control Centers An Architectural View IWSM MetriK on Mensura 2008 pp 302 315 Munich Germany 2008 11 J OHNSON2004 P M Johnson
157. ogle 10000000000000000 0000000000 2220 0 000000 engineering O O 3 6500 0 software engineering center 210 0 0 000000000 L HOOG 00 EE LI LI 00060 006 6 006 6 8 SEC journal Vol 5 No 1 2009 IP AN 0 E E BEE 10600 00000000000 00000 0 00 0 00000000000000000005 HE Hailie E SH EE Med C I DESEE HEEL HSH SEIS HEEL E HSE E 8 OE Pp mua P D C IR H k BE lt 280 Phasa n 0000000000000000000000000 2123 T PER
158. ol 5 2009 29 00000000000 00000000000 270 2000000000000000000 0000 LU LI LI D D DEI 0000000000000 1000 0000 00 xxxxxxxx 1 000000 00005000 000 Service Offerings 0 SWETH DI 000 U 00 0 24 000000 00000000000000000000000000000 3 00 00 0000000000000 Web Applicationt I DO 00000 4 0 000000000000000 000000000000000 000000 000000000000 000000000060 5 OSW SWIJ 00000 Service Offeringsl SW
159. rding to some criteria and to build models for each stratified projects In this paper we propose to stratify projects by project attributes programming language and architecture et which have strong relationships to productivity when building effort estimation models We analyzed 43 projects data selected from 153 projects collected by Economic Research Association and concluded that team size per project size had strongest relationship to productivity variance explained was 38 Although in general size per project size is fixed when estimating effort it is not very difficult that size per project is fixed approximately We stratified projects by team size per project size and built models for each stratified projects As a result the accuracy of the models showed better performance than a model built from whole projects and the median of relative error of estimated effort was improved from 566 to 359 when the average of relative error of estimated team size was 50 1 1981 SHEPPERD1997 SRINIVASAN1995 TSUNODA 2005 0 0000000000000000000000000
160. svillf 00000000000 11 Conirol Cete 12 Mesara Inendiond Software Process and Product Meesuremert 13 SOFTPIT Workshop on for Distributed Software and Syers Enginegring Projads SEC journal Vol 5 No 1 Mar 2009 14 Asa Padfic Software Engnesing Conference 15 ATGSE WarksoponAcuurtehility and in Global Software x 16 SESARLab Software Enginesing and Software Advanced Research of Departrmert of Infomeion TeTrology of Milan U U 6 Sense Analytics 2009010 Rally Software 8 Han niq 5 850 000000 ENSE ERN 00000000 2000000000000 4 ED DH ED P s d e EDU RETE o d U LL EU D BL ET E D C EPMO 0 20030 40 20080 3D 100000000000000000 s EVER EAS 000 00000 0 0 000 0000000
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