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1_PredictingUnderstandability_Malik_Boehm_Brown

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Predicting Understandability of a Software Project Using COCOMO II Model DriversOutlineIntroductionRUP Hump ChartEmpirical StudySlide 6Motivation & Related WorkMotivation & Related Work (2)MethodologyMethodology (2)Methodology (3)Methodology (4)Methodology (5)Methodology (6)Methodology (7)Methodology (8)Methodology (9)ResultsResults (2)Future WorkReferences10/27/2008 ©USC-CSSE 1University of Southern CaliforniaCenter for Systems and Software EngineeringPredicting Understandability of a Software Project Using COCOMO II Model DriversAli Afzal MalikBarry Boehm A. Winsor Brown{alimalik, boehm, awbrown} @usc.edu23rd International Forum on COCOMO and Systems/Software Cost Modeling10/27/2008 ©USC-CSSE 2University of Southern CaliforniaCenter for Systems and Software EngineeringOutline•Introduction•Motivation & Related Work•Methodology•Results•Future Work•References10/27/2008 ©USC-CSSE 3University of Southern CaliforniaCenter for Systems and Software EngineeringIntroduction•Understandability–“Degree of clarity of the purpose and requirements of a software system to the developers of that system at the end of the Inception phase”•Basic idea–Quantification enables prediction–Reuse inputs of software cost estimation•Empirical Study–Projects10/27/2008 ©USC-CSSE 4University of Southern CaliforniaCenter for Systems and Software EngineeringRUP Hump Chart(Kruchten, 2003)10/27/2008 ©USC-CSSE 5University of Southern CaliforniaCenter for Systems and Software EngineeringEmpirical Study•SE I (Fall) and SE II (Spring)•2004 – 2007•24 real-client, MS-student, team projects (SE I 2008, SE II 2008)•Process: MBASE/RUP (Boehm et al. 2005, Kruchten 2003)•Projects–Development-intensive–Used COCOMO II10/27/2008 ©USC-CSSE 6University of Southern CaliforniaCenter for Systems and Software EngineeringS# Year Project Type1 2004 Bibliographies on Chinese Religions in Western Languages Web-based database2 2004 Data Mining of Digital Library Usage Data Data mining3 2004 Data Mining from Report Files Data mining4 2005 Data Mining PubMed Results Data mining5 2005 EBay Notification System Stand-alone application6 2005 Rule-based Editor GUI7 2005 CodeCount™ Product Line with XML and C++ Code Counter Tool8 2006 California Science Center Newsletter System Web-based database9 2006 California Science Center Event RSVP System Web-based database10 2006 USC Diploma Order/ Tracking Database System Web-based database11 2006 USC Civic and Community Relations web application Web-based database12 2006 Student's academic progress web application Web-based database13 2006 New Economics for Woman (NEW) Web-based database14 2006 Web Portal for USC Electronic Resources Web-based GUI15 2006 Early Medieval East Asian Tombs Web-based database16 2006 USC CONIPMO Cost model17 2006 An Eclipse Plug-in for Use Case Authoring Stand-alone application18 2007 USC COINCOMO Cost model19 2007 BTI Appraisal Projects Stand-alone database20 2007 LAMAS Customer Service Application Web-based database21 2007 BID review System Stand-alone database22 2007 Proctor and Test Site Tracking System Web-based database23 2007 E-Mentoring program Web-based application24 2007 Los Angeles County Generation Web Initiative Web-based database10/27/2008 ©USC-CSSE 7University of Southern CaliforniaCenter for Systems and Software EngineeringMotivation & Related Work•Some important considerations–18% of software project failures due to unclear objectives and incomplete R&S (Standish Group 1995)–Escalation in cost of fixing requirements defects: rapid for large and considerable for smaller projects (Boehm 1981, Boehm and Turner 2004)–Requirement changes have significant impact on project’s budget and schedule (Zowghi and Nurmuliani 2002)10/27/2008 ©USC-CSSE 8University of Southern CaliforniaCenter for Systems and Software EngineeringMotivation & Related Work (2)•An objective mechanism to predict understandability enables–Minimization of resource wastage due to rework–Answering “How much RE is enough?”•Related previous work–“Expert COCOMO” (Madachy 1997)•Uses COCOMO II cost factors to quantify risk10/27/2008 ©USC-CSSE 9University of Southern CaliforniaCenter for Systems and Software EngineeringMethodology•Identified 8 relevant COCOMO II model driversS# Model Driver Description1 PREC Product precedentedness2 RESL Architecture/Risk resolution3 CPLX Product complexity4 ACAP Analyst capability5 PCAP Programmer capability6 APEX Applications experience7 PLEX Platform experience8 LTEX Language and tool experience10/27/2008 ©USC-CSSE 10University of Southern CaliforniaCenter for Systems and Software EngineeringMethodology (2)•Weighted-sum formula–UNDR – understandability–MDi – ith Model Driver’s value–wi – weight of MDi–ni – nature of MDi; Є {-1, +1}•-1 for CPLX; +1 for the rest81iiiiMDwnUNDR10/27/2008 ©USC-CSSE 11University of Southern CaliforniaCenter for Systems and Software EngineeringMethodology (3)•Model driver rating scaleDriver Rating Rating Symbol Numerical ValueVery Low VL 1Low L 2Nominal N 3High H 4Very High VH 5Extra High XH 610/27/2008 ©USC-CSSE 12University of Southern CaliforniaCenter for Systems and Software EngineeringMethodology (4)•Voting for model driver weights–22 students from SE II class–Rating scale•1 (least important) – 5 (most important)i MDiwi1 PREC 3.642 RESL 3.233 CPLX 3.644 ACAP 3.555 PCAP 3.956 APEX 3.647 PLEX 3.328 LTEX 3.3610/27/2008 ©USC-CSSE 13University of Southern CaliforniaCenter for Systems and Software EngineeringMethodology (5)•Determine the lowest and highest numerical values of understandability81)min(iiiiLowMDwnUNDR81)max(iiiiHighMDwnUNDR10/27/2008 ©USC-CSSE 14University of Southern CaliforniaCenter for Systems and Software EngineeringMethodology (6)•min() and max()min (MDi) {if (ni = = +1) return minimum numerical value of MDi else return maximum numerical value of MDi } max (MDi) {if (ni = = +1) return maximum numerical value of MDi else return minimum numerical value of MDi }10/27/2008 ©USC-CSSE 15University of Southern CaliforniaCenter for Systems and Software EngineeringMethodology (7)86.2LowUNDR64.126HighUNDR i MDiniwimin(MDi) max(MDi)1 PREC +1 3.64 1 62 RESL +1 3.23 1 63 CPLX -1 3.64 6 14 ACAP +1 3.55 1 55 PCAP +1 3.951 56 APEX +1 3.64 1 57 PLEX +1 3.32 1 58 LTEX +1 3.36 1 510/27/2008 ©USC-CSSE 16University of Southern CaliforniaCenter for Systems and Software


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