APPENDIX A. Literature Review Resources

Transcription

1 APPENDIX A. Literature Review Resources Various literature resources were reviewed related to object-oriented approach, maintainability, usability software quality models in systematic manner by following the guidelines of Kitchenham et al. (2004). Only those sources included in the review process, which showed some direct evidence related to research objectives described in Chapter 1. The search strategy was based on the inclusion of conferences, journals, stards, models, technical reports books. Following Table represents major online resources referred for review. Table A.1: Online Resources for Literature Review SOURCES Digital Libraries / Electronic Databases Electronic Journals SPECIFIC RESOURCES IEEExplore (ieeexplore.ieee.org) Springer Link (springerlink.com) CiteSeer (citeseer.ist.psu.edu) ACM digital library (portal.acm.org) Scopus (scopus.com) DBLP Computer Science Bibliography (dblp.uni-trier.de) Taylor Francis (taylorfrancis.com) Google (scholor.google.com) IEEE Transactions on Software Engineering IEEE Software IEEE Computer Communications of the ACM ACM SIGSOFT Software Engineering Notes Software Quality Journal (Springer) Empirical Software Engineering (Springer) Journal of Systems Software (Elsevier)

2 Class Coupling Cohesion Inheritance Size Polymorphism References APPENDIX B. Comparison of Object Oriented Metrics CC AMC Lyu (1992) DAC, DAC, MPC Li Henry (1993) WMC CBO, RFC LCOM, DIT, NOC NOD, NOP NMA Chidamber Kemerer (1994) Lake Cook (1994) CS, NOO, NOA NMO, NMINH, SIX, NMI NIM, NCM, NPAVG, NMA Lorenz Kidd (1994) CF ICP, IH-ICP, NIH-ICP AHF, MHF LCC, TCC LCOM3, LCOM4, CO ICH MIF, AIF PF Abreu (1995, 1996) Bieman Kang (1995) Hitz Montazeri (1995) Lee et al. (1995) NOM MPC, CTA, CTM Li et al. (1995) NOD, NOA Tegarden et al. (1995)

3 LCOM5 AID Henderson Sellers (1996) CDM CHNL Binkley Schach (1998) IFCAIC, ACAIC, OCAIC, FCAEC, DCAEC, OCAEC, IFCMIC, ACMIC, OCMIC, FCMEC, DCMEC, OCMEC, IFMMIC, AMMIC, OMMIC, FMMEC, DMMEC, OMMEC NAS NAI Bri et al. (1998) Harrison et al. (1998) NAC, NLM, CTA, CTM NOO Li (1998) NDC CACI, CI, CMIC CACL, CL, CMICL Nesi Querci (1998) DCC, MOA ANA Bansiya (99) SPA, DPA, SPD, DPD, SP, DP, NIP, OVO Benlarbi Melo (1999)

4 CBM, IC NOMA, AMCTKC Tang et al. (1999) NAINH NAINH NMImp, NMInh, NM, NAImp, Totattrib, NumPar, Stmts, NMpub, NMNpub, Attrib, States, EVNT, READS, DELS, RWD, LOC, LOC_B, LOC_H DAM, MO, FRIEND Cartwright Shepperd (2000) Bieman et al. (2001) CBOback, CBOforward, CFF, CBB Wilke Kitchenham (2001) MFH, MFA, MAA, MOA, MOS, HRM, DAH, OAM, MAM, NOC, NOA, NOM, CIS, CSB, CSM CAM DOI, ANA NOP Bansiya Davis (2002)

5 CBOin, CBOout, RFCin, RFCout MaxHAgg, Nagg, NAggH, NGenH Yu et al. (2002) Genero et al. (2003) ACD, NOI, NOCU Zimmermann et al. (2007)

6 APPENDIX C. Comparison of Maintainability Evaluation Methods Variables/Metrics Used Methods Used Dataset used Source AveLOC (Average Line Of Code), ES (Executable Statement), LC (Line of Comment), NES (Number of Executable Statement) DIT (Depth of Inheritance Tree), NOC (Number Of Children), CBO (Coupling Between Object), RFC (Response For a Class), LCOM (Lack of COhesion of Method), WMC (Weight Method per Class) MAT (Maintainability Analysis Tool), Regression, Halsted metrics, Cyclomatic Complexity, Assessment Model, Entropy Linear, Regression Analysis HP-MAS(Hewett Packard- Maintainability Assessments System)by University of Idaho Software Engineering lab, AFOTEC Instrument Local data sets Zhuo et al. (1993) Li et al. (1993) CBO (Coupling Between Object classes), LOC (Line Of Code) Linear Regression Analysis 2 ADA system(uims,qu ES commercial ) Henry et al. (1995) CDM (Coupling Dependency Metric), CBO (Coupling Between Object classes), NSSR (Number of Sub System Relationship), RFC (Response set For a Class), WMC (Weight Method per Class), DIT (Depth of Inheritance Tree), CHNL (Class Hierarchy Nested Level), NCIM (Number of Class Inheriting a Method), WIH (Width of Inheritance Hierarchy), HIH (Height of Inheritance Hierarchy) Class Coupling C++ system (patient core management), 113cls,82KLOC,f ile transfer facility,29 java classes, 6 KLOC Binkley et al. (1998)

7 Impact Rate, Effort, Error Rate, Subjective Evaluation, Goodness-of-fit statistics test, Regression coefficient test, Multidimensional Assessment, Albrecht Metrics, Software Complexity Metric, Card agresti's Complexity metric Regression Analysis MAT(Maintainabi lity Analysis Tool)using FLECS(a Structured Fortran preprocessor Muthanna et al. (2000) SLA (Service Level Agreement), KLOC (Kilo Line of Code), TRCA (Time of Resolution of Critic Anomalies), MR (no. of Modification Request), UC (Urgent Corrective) Logistic Regression, MANTEMA a methodology for maintenance developed by atos ODS Using C++,where the context is very different i.e. not available in COBOL (i.e. pointers) Hayes et al. (2005) KLOC (Kilo Line of Code), LCOM (Lack of Cohesion in Method), LCC (Loose Class Cohesion), TNOS (Total No. Of Statement), DIT (Depth of Inheritance Tree), ICAIC (Inheritance Class- Attribute Import Coupling), NICAIC (Non-Inheritance Class-Attribute Import Coupling), ICAEC (Inheritance Class- Attribute Export Coupling), NICMIC (Non Inheritance Class Method Import Coupling), NIMMIC (Non Inheritance Method-Method Import Coupling), IIC (Inheritance Import Coupling), IEC (Inheritance Export Coupling) DTRIX parser used to assess maintainability aspects of object oriented software Java Systems FUML dynamic object browser(dobs) Dagpinar et al. (2003)

8 KA (Key Abstraction), VOPC (View Of Participating Classes), UML (Unified Modeling Language), PCA (Principal Component Analysis), NC (No. of Classes), NA (No. of Attributes), NAGG (No. of Aggregation), NDEP (No. of Dependencies), MP (Maintainability Products), CF (Coupling Factor), CR (Comment Ratio), Hdiff (Halstead difficulty), LCOM (Lack of COhesion in Methods), AC (Attribute Complexity), CC (Cyclomatic Complexity), TCR (True Comment Ratio), PM (Perceived Maintainability), LOC (Line Of Code) Understability, Modifiability, UML (Unified Modeling Language) CR (Comment Ratio), AC (Attribute Complexity), LOC (Line Of Codes), TCR (True Comment Ratio), MI (Maintainability Index), LOC (Line Of Codes) Number of methods, Number of association Linear Regression COCOMO Constructive Cost estimation Model), SLIM, AMEffMo(Adapti ve Maintenance Effort Model), Regression Analysis Association, Aggregation, Generalization, Classification Regression Analysis DC Ratio, Finding the replicated data form from data description using ANOVA method Validation dataset (the residue even increase as DLOC increase) Multilayer perceptron decision trees (applied to construct the maintainability) Spathic Project Data from source code a test generation tool Genero et al. (2003) Hayes et al. (2004) Kiewkanya et al. (2004) Hayes et al. (2005) Linear model Local dataset Bocco et al. (2005)

9 MA (Multi-criteria Analysis), WMC (Weighted Methods Per Class), NPM (Number of Public Methods), CBO (Coupling Between Objects), NOP (No. Of Polymorphic methods), DIT (Depth of Inheritance Tree), LCOM (Lack of COhesion in methods) K-Attractors, Code-level metrics Class Diagram, LOC (Line Of Code), MI (Maintainability Index) NC (Number of Class) NA (Number of Attributes) NM (Number of Methods) LOC (Line Of Code), CP (Change Pattern), ROC (Receiver Operating Characteristics area) class diagram, LOC (Line Of Code), DLOC (Difference Line Of Code), NC (Number of Classes), NA (Number of Attributes), NM (Number of Methods) LOC (Line Of Code), DLOC (Difference Line Of Code), MI (Maintainability Index), CC (Cyclomatic Complexity) Data Extraction Process, Clustering Maintainability Data Extraction Process, Multimedia GIS(Geographic Information Services) Regression Methodology to analyze 1440 classes of APACHE Geronima Data from ISO/IEC 9126 for reliability testability Antonellis et al. (2007) Antonellis et al. (2009) Rizvi et al. (2010) Linear Local data sets Arisholm et al. (2010) Linear Regression model Multivariate Linear model Data collection from multivariate maintainability modifiability models F-Test for multivariate analysis Makker. (2010) Gautam et al. (2011)

10 APPENDIX D. Usability Concepts Researchers Foley Van Dam (1982) Smith Moiser (1984) Eason (1984) Gould (1985) Shackel (1986) Tyldesley (1988) Doll &Torkzadeh (1988) Ravden & Johnson (1989) Igbaria & Parasuraman (1989) Booth (1989) Polson & Lewis (1990) Holcomb & Tharp (1990) Shackel (1991) Mayhew (1992) Grudin (1992) Usability Concepts User interface guidelines. Described usability as product s attribute. Interrelated usability functionality. Defined usability in terms of learnability, usefulness ease of use. Defined usability with the factors effectiveness, learnability, flexibility attitude. Mentioned 22 factors that could be used to build the metrics specifications. End User Computing Satisfaction Instrument (EUCSI). Presented software inspection as usability evaluation mechanism. Enjoyability is directly proportional to acceptance of a system He modified Shackel s criteria into usefulness, effectiveness, learnability, attitude. He gave problem solving strategies for novice users to interact with the complex interface. Presented a software usability model for the system designers to decide which usability sub attributes should be included. Elaborated the usability concept. Reviewed usability principles to describe the desirable properties of the interface. Practical acceptability of the system within the various categories like cost, support, system usefulness.

11 Nielsen (1993) Dumas & Redish (1993) Dumas &Redish (1994) Preece et al. (1993) Lowgren(1993) Hix & Hartson (1993) Nielsen & Levy (1994) Logan (1994) Caplan(1994) Bevan (1995) Lamb (1995) Guillemette (1995) Kurosu & Kashimura(1995) Nielsen (1995) Botman (1996) Butler (1996) Harrison & Rainer (1996) Presented usability heuristics for the inspection method of usability evaluation. He classified usability to, learnability, efficiency, memorability, errors, satisfaction. explained their definition of usability on the basis of focus on users, usability means, use of product by users for productivity, users are busy people trying to accomplish tasks, decision of user about when the product is easy to use. people who use the product can produce them so quickly easily in order to accomplish their own tasks. Related usability to overall performance of the system user satisfaction. Explained usability as the outcome of relevance, efficiency, learnability attitude. Related usability to the interface efficiency also to user reaction to the interface. Worked on user satisfaction assessment of product. divided usability into social emotional dimension Defined apparent usability as an important consideration in the design of a software system. Usability replaced by quality in use Claimed usability as a wider concept which includes content usability, organizational usability inter organizational usability. Reviewed defined usability with respect to effective use of information system. Divided usability into Inherent usability Apparent usability. Presented Discount usability engineering. Presented Do it yourself usability evaluation. Dealt with usability engineering. Reviewed a model used for computing satisfaction EUCSI.

12 Kanis & Hollnagel (1997) Gluck (1997) Tractinsky (1997) Lecerof & Paterno (1998) Thomas (1998) ISO (1998) Clairmont (1999) Head (1999) Veldof et al.(1999) Verdonckt (1999) Kengeri et al. (1999) Squires & Preece (1999) Arms (2000) Alred et al. (2000) Battleson et al.(2001) Hudson (2001) Turner(2002) Blford &Buchanan (2002) High degree of usability can be determined when the error rate of usability is minimum. Correlated Usability to usefulness usableness. Contributed in explaining the concept of Apparent usability. Declared functionality being essential to usability. Categorized usability sub attributes into three categories: outcome, process, task. Guidance on usability which discusses usability for the purposes of system requirement specifications its evaluation. Usability is the degree to which a user can successfully learn use a product to achieve a goal. Usability is rooted in cognitive science - the study of how people perceive process information through learning, the use of memory, attention Related usability, user s reaction system development Design guidelines principles to build an effective user friendly interface. Explained usability using effectiveness, likability, learnability usefulness. Usability concept was regarded for pedagogical value for e- learning systems. Aspects of usability that are interface design, functional design, data metadata, the computer systems networking. Related usability to technical/system human factors. Explained interface design that is easy to learn, remember, use, with few errors. The concept of web usability was described. Illustrated a checklist for the evaluation of usability. Explained usability in terms of technical, cognitive, social design. Also, looked into the future work on methods for analyzing usability.

13 Palmer (2002) Oulanov & Pajarillo (2002) Matera et al. (2002) Guenther (2003) Pack (2003) Campbell & Aucoin (2003) Abran et al.(2003) Quesenbery (2001, 2003) Villers (2004), Dringus & Cohen (2005), Miller (2005) Shneiderman Plaisant (2005) Krug(2006) Dee & Allen (2006) Seffah, Kline & Padda (2006) Brophy & Craven (2007) Tullis & Albert (2008) Tullis (2009) Gardner-Bonneau (2010) Explained usability in context of web usability. Interface effectiveness as one of the most important aspects of interaction. Gave Systematic usability evaluation. Illustrated the difficulties in defining usability. Explained usability as a relationship between tools its users. Referred usability as a set of multiple concepts, performance of the system, execution time of a specified task, user satisfaction ease of learning. Presented the five Es of usability which include effectiveness, efficiency, engagement, error tolerance, ease of learning. Expressed that usability evaluation methods should consider pedagogical factors. Guidelines for error prevention, discussed the system s response time, data entry within HCI. Studied usability from the user s perspective based on their experience. End-user interface conforms to usability principles. Gave 10 usability factors namely, efficiency, effectiveness, productivity, satisfaction, learnability, safety, trustfulness, accessibility, universality, usefulness are associated with twenty-six usability measurement criteria. Explained web usability Presented Tips Tricks for Measuring the User Experience. Explained Top Ten Myths about Usability. Explained the effectiveness sustained by the software system when technical changes are made to it.

14 Bergstrom et al. (2011) Conducted iterative usability testing.

15 APPENDIX E. Usability Attributes in Different Stards Models Attributes Source operability, training, communicativeness McCall s (1977) ease of use, effectiveness, learnability, flexibility, user attitude Shackel (1981, 1986,1991) task, predefined time Butler (1985) user satisfaction, type of errors Makoid et al. (1985) ease of learn, ease of use Reed (1986) system performance, system functions, user interface Gould (1988) usefulness, effectiveness, learnability, attitude Booth (1989) product, user, ease of use, acceptability of product human factors, aesthetics, consistency in the user interface, online context sensitive help, wizards agents, user documentation, training materials Bevan et al. (1991) Grady (1992) comprehensibility, ease of learning, communicativeness factors IEEE Std (1992) Dumas et al. users, productivity, tasks, ease of use (1993) initial performance, long-term performance, learnability, Hix et al. (1993) retainability, advanced feature usage, first impression, long term user satisfaction result of relevance, efficiency, learnability, attitude Löwgren (1993) learnability, efficiency, memorability, few errors, satisfaction Nielsen (1993) efficiency, affect, helpfulness, control, learnability Porteous et al. (1993) learnability, throughput, attitude, flexibility Preece (1994) system usefulness, information quality, interface quality Lewis (1995) usableness, usefulness Gluck (1997) learnability, efficiency, memorability, satisfaction, flexibility, first Wixon (1997) impressions, advanced feature usage, evolvability learnability, flexibility, robustness Dix et al. (1998) efficiency, effectiveness, satisfaction ISO (1998)

16 users needs, efficiency, users subjective feelings, learnability, system s safety Lecerof et al. (1998) outcome, process, task Thomas (2009) learnability, efficiency in use, rememberability, reliability in use, user satisfaction effectiveness, likeability, learnability, usefulness interface design, functional design, data metadata, computer systems network easy to learn, rememberability, few errors, support effectiveness, efficiency, satisfaction, productivity, safety, internationality, accessibility understability, learnability, operability, attractiveness, usabilitycompliance functionally correct, efficient to use, easy to learn, easy to remember, error tolerant, subjectively pleasing Constantine (1999) Kengeri et al. (1999) Arms (2000) Battleson et al. (2001) Donyaee et al. (2001) ISO/IEC (2001) Brinck et al. (2002) interface effectiveness Kim (2002) affect, efficiency, control, helpfulness, adaptability Oulanov (2002) modifiability, scalability, reusability, performance, security easy to learn, easy to use, easy to remember, error tolerant, subjectively pleasing time to learn, speed of performance, rate of errors by users, retention over time, subjective satisfaction task times, completion rates, errors, post task satisfaction, post-test satisfaction Bass et al. (2003) Campbell et al. (2003) Shneiderman Plaisant (2005) Sauro et al. (2009)

17 Evaluation Method Type Evaluation Method Applicable Stages Description Pros Cons APPENDIX F. Comparison of Usability Measurement Methods Coaching method Design, code, test deploymen t Collects information about the needs of the user Coach is easy to find users usage problems on the spot Overall interaction between coach users is not so good they find less usability problems Testing Performance Measurement Design, code, test deploymen t Collects information about performanc e of an organizatio n or an individual Compares different interfaces checks if aim of the user has been met or not It gives emphasis to first time usage covers only a limited number of interface features Question Asking Protocol Design, code, test deploymen t Users ability to answer questions is checked It is simple through this protocol we know what parts of interface were obvious what were not Interpretati on for this can be wrong Retrospectiv e Testing Design, code, test deploymen It gives a walkthroug h of the performanc Used for participants for whom talking or It is time consuming

18 t e recorded previously on video. writing working may be difficult Thinking aloud protocol Design, code, test deploymen t It is conducted with experiment ers who videotape the subjects perform detailed protocol analysis. It is not so expensive the results are close to the observation s made by users It is not user friendly protocol Testing Co- Discovery Learning Design, code, test deploymen t It involves observation of two users working on same task. Users feel free to discuss with each other Difference in learning culture style may affect the feedback Teaching Method Design, code, test deploymen t Used as an alternative to think aloud method Number of verbalizati ons are more hence the participant interactive behavior provides the participants though process search strategy It is time consuming since briefing the participants is necessary Remote Testing Design, code, test deploymen t Testers can view user s interaction in real time Three major issues (effectiveess, efficiency An additional software is also required to observe

19 satisfaction) of usability are covered users from distance Cognitive walkthrough Design, code, test deploymen t A group of experts examine the code in a certain pattern to search problems It does not require a fully functional prototype It does not address user satisfaction as well as efficiency Heuristic Evaluation Design, code, test deploymen t Finds usability problems in user interface No need of formal training required for evaluators Biased by preconcepti ons of evaluators Inspection Feature Inspection Code, test deploymen t It lists features used to accomplish tasks. It does not require large number of evaluators. Takes a long time if applied for all features of the system Pluralistic Walkthrough Design Identifies usability issues in a piece of software more number of usability can be found at a time The most important issue of usability i.e. efficiency is not addressed Card Sorting User requirements early design Technique that involves users to group information for a web site. It is simple, organized, cheap fast to execute Results of card sorting may vary

20 Tree Testing user requirements design Reverse of card sorting Allows to test navigation visually thus it ensures reliability It is not moderated thus researchers cannot see users or participants Field - observation Test deploymen t Collects detailed information about how people work the context in which works takes place. It is highly reliable less expensive Some task may not be in the manner they are observed Inquiry Interviews/ Focus groups Context user requirements testing Takes out views understi ngs of the users about a selected topic Useful ideas are produced which also results in healthy customer relations Data collected has low validity Proactive Field study Requireme -nt design It is used in early design stage to underst the needs of the users Individual users characterist ics, task analysis functional analysis is found It cannot be conducted remotely collected data is not quantitative Logging Actual use Test deploymen t Automatica lly collect statistics about the detailed use of the system It can show the statistics of each action It shows what users did not why they did it

21 Surveys Test deploymen t Acquire information focused directly on problems conclusions. It is comparativ ely faster in determinin g preference of large user groups It does not capture details may not even permit the followup SUMI Questionnaire User requiremen ts testing It is a method of measuring quality from user s point of view Provides objective way of assessing users satisfaction Results produced by SUMI are only valid if questionnai re has been administere d in same way to all users if results are interpreted properly carefully. MUSiC Context Analysis It aims on achieving qualitative quantitative data to support the systems It is used to find performanc e metrics of the user It fails to capture an accurate reviewable record DRUM Video Recording It finds diagnostic information from an analysis of videotape It helps analyst to create a time log of the user actions it needs to be licensed to organizatio ns because of the risks involved.

22 APPENDIX G. Usability Questionnaire SECTION 1. Dear Sir/Madam, This questionnaire has been prepared to measure the importance of proposed usability factors of the software systems. The aim of this questionnaire is to gather software projects specific data in order to evaluate usability of object-oriented system. Within the context of this project, Usability is a software quality factor that defined as the ease with which a user can learn to operate, prepare inputs for, interpret outputs of a system or component. Usability Sub-Factors Effectiveness: It can be described as a performance measure of the system to complete a specified task or goal successfully within time Efficiency: It can be described as the extent of successfulness of a task by a system. Its concept is related to accuracy completeness of the specified goals. Satisfaction: It can be described as the user s acceptability of the system, in the specified context of use. Learnability: It can be described as the capability of the software product to enable the user to learn its application The above mentioned characteristics can be measured using the following software metrics: 1. Weighted Methods per Class (WMC) It is the count of methods implemented within a class or the sum of complexities of the methods. 2. Depth of Inheritance (DIT)

23 It is the number of steps from the class node to the root of the tree is measured by number of ancestor classes. 3. Coupling between Object Classes (CBO) CBO is a count of number of other classes to which a class is coupled. It is measured by number of distinct non-inheritance related class hierarchies on which a class depends. 4. Responses for a Class (RFC) Count of the set of all the methods that can be invoked in response to a message to an object of the class or by some method of the class. 5. Lack of Cohesion (LCOM) Cohesion is the degree to which the methods in a class are related to one another. LCOM measures the dissimilarity of methods in a class by instance variable or attributes. 6. Number of Children (NOC) NOC is defined as the number of immediate child classes derived from a base class. Note: The values of all the above metrics are inversely proportional to the usability of system. Guidelines to fill the Questionnaire (i) The following scale (intensity of importance) is to be referred for filling in the matrices in the Questionnaire: Intensity of Definition Explanation Importance 1 Equal Importance Two Factors contributes equally to the objective 3 Somewhat more Important Experience judgment slightly favor one over the another 5 Much more important Experience judgment strongly favor one over the another 7 Very much more important Experience judgment very strongly favor one over another. Its Importance is demonstrated in practice. 9 Absolutely more The evidence favoring one over the other is of the

24 important highest possible validity. Table G.1: Scale for Relative Importance (ii) An Example to demonstrate the procedure Note: Each entry in the matrix denotes the performance/importance of the factor given in the row as compared to the factor given in the corresponding column. A matrix for pair wise comparisons of 4 factors (A, B, C D) affecting Usability of a system is given below. Let us assume the following criteria for filling the matrix given below: 1. factor A is somewhat more important than factor B, hence value is factor C is much more important than factor A. Then, factor A must be much less important than factor C. Hence value is 1/5. 3. factor C be absolutely more important than factor B. Then, factor B must be absolutely less important than factor C. Hence value is 1/9. By referring to Table G.1, we get the following values: USABILITY A B C A 1 3 1/5 B 1 1/9 C 1 NOTE: The data you provide as part of this survey will be used in the research will be kept confidential. The details of any participating respondent will not be directly published anywhere without prior permission.

25 QUESTIONNAIRE SECTION 2 Company s Name (Optional): Respondent s Name (Optional): Respondent s Designation (Optional): Domain/Specialization Area: PAIRWISE COMPARISON OF ATTRIBUTES Based on your perception, please fill in the matrices 1, 2, 3, 4 5 by rating each attribute from 1-9 according to the above-mentioned guidelines Ttable G.1. MATRICES: 1. PAIRWISE COMPARISON Effectiveness Efficiency Satisfaction Learnability Effectiveness 1 Efficiency 1 Satisfaction 1 Learnability 1 2. EFFECTIVENESS WMC RFOC LCOM DIT NOC CBO WMC 1 RFOC 1

26 LCOM 1 DIT 1 NOC 1 CBO 1 3. EFFICIENCY WMC RFOC LCOM DIT NOC CBO WMC 1 RFOC 1 LCOM 1 DIT 1 NOC 1 CBO 1 4. SATISFACTION WMC RFOC LCOM DIT NOC CBO WMC 1 RFOC 1 LCOM 1 DIT 1 NOC 1 CBO 1 5. LEARNABILITY WMC RFOC LCOM DIT NOC CBO WMC 1 RFOC 1 LCOM 1 DIT 1 NOC 1

27 APPENDIX H. Snap Shot of the Values of CK Metrics H.1 CK Metrics for Project1

28 H.2 CK Metrics for Project2