Descartes Greatest Argument Mind Body Duality

Descartes’ Greatest Argument: Mind-Body Duality Man is not merely an animal, but rather a body and soul that lives forever. This is an idea that took centuries to develop, and one that still conflicts the great thinkers of our time. Descartes, who ushered in the modern age of philosophy and who is arguably the greatest questioner of Aristotle, develops his ideas on the mind body duality throughout his first six Meditations. Explaining the essential characteristics of thinking things in contrast with those of physical things, Descartes makes it clear why the mind cannot simply be the brain and why, although we physically are present, our essence, our existence, is not linked with our physical self. Descartes arguments, however well crafted†¦show more content†¦Descartes presents ideas that could possibly prove the mind-body singularity. He realizes he perceives colour and sound through his senses and also with the help of his memory. This means that these perceptions appear to have reached his imagination. Descartes specul ates that the things he perceives may help prove that bodies exist. However, he soon realizes that it was in fact external bodies that caused him to have these perceptions, realizing that his senses have deceived him since he has had the same perceptions whilst being awake as well as when he is dreaming. Sensory experiences are equally present in ones’ dreams, yet the senses are not being activated. All that can be certain is the experience, not that the experience is the result of the contribution from ones’ senses. With this newly developed idea, experiences, independent of their cause, can be nothing more than a mere form of thinking and therefore a product of one’s imagination (25), meaning they are entirely independent of a body, and so do not require one. This is a first advancement on the idea of the mind-body duality. Descartes proceeds to bring forward the idea that all that is immediately available to the mind is perceptual experience. Regardless of w hat lies beyond that experience in the external world, Descartes believes that it is very certain that he sees light, hears noise and feels heat; and these are all a property of perception, and this, when interpreted in this precise sense, can be

The Activism Towards The German Unification - 1512 Words

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Comparing Extreme Programming and Waterfall Project Results Free Essays

Comparing Extreme Programming and Waterfall Project Results Feng Ji Carnegie Mellon University Silicon Valley Campus Mountain View, CA, 94035 jojojifeng@gmail. com Todd Sedano Carnegie Mellon University Silicon Valley Campus Mountain View, CA, 94035 todd. sedano@sv. We will write a custom essay sample on Comparing Extreme Programming and Waterfall Project Results or any similar topic only for you Order Now cmu. edu Abstract Waterfall and Extreme Programming are two software project methods used for project management. Although there are a number of opinions comparing the two methods regarding how they should be applied, none have used project data to clearly conclude which one is better. In this paper, we present the results of a controlled empirical study conducted at Carnegie Mellon University in Silicon Valley to learn about the effective transition from traditional development to agile development. We conducted a comparison research against these two approaches. Multiple teams were assigned a project; some used Waterfall development, others used Extreme Programming. The purpose of this research is to look at advantages and disadvantages based upon the outcomes, generated artifacts, and metrics produced by the teams. 1. Introduction 1. 1. Agile vs Traditional Since the early 1970s, numerous software managers have explored different ways of software development methods (such as Waterfall model, evolutionary model, spiral model etc. ) those have been developed to accomplish these goals and have been widely used by the software industry [1]. Methodologists often describe the Waterfall method as a stereotypical traditional method whereas they describe Extreme Programming as the stereotypical agile method. The Waterfall model, as the oldest traditional software development method, was cited by Winston W. Royce in 1970 [2]. He divided the software development lifecycle into seven sequential and linear stages: Conception, Initiation, Analysis, Design, Construction, Testing, and Maintenance. The Waterfall model is especially used for large and complex engineering projects. Waterfall’s lasting impression upon software engineering is seen even in the Guide to Software Engineering Body of Knowledge which introduces the first five knowledge areas based upon their sequence in the Waterfall lifecycle even though the Guide does not recommend any particular lifecycle [3]. Although the Waterfall model has been adopted in many large and complex projects, it still has some inherent drawbacks, like inflexibility in the face of changing requirements [1]. If large amounts of project resources have been invested in requirements and design activities, then changes can be very costly later. High ceremony documentation is not necessary in all projects. Agile methods deal well with unstable and volatile requirements by using a number of techniques of which most notable are: low ceremony documents, short iterations, early testing, and customer collaboration. Kent Beck and Cynthia Andres define Extreme Programming 2. 0 with many practices [4], like Pair Programming, Test First Programming, and Continuous Integration and so on. These characteristics enable agile methods to obtain the smallest workable piece of functionality to deliver business value early and continually improving it while adding further functionality throughout the life of the project [5]. 1. 2. PET project background Carnegie Mellon University Silicon Valley students start their masters program with the Foundations of Software Engineering course. This course is team-based, project-based, and mentored. Each team builds The Process Enactment Tool (PET). The user personas are software developers and managers. The tool helps users plan, estimate, and execute a project plan while analyzing historical data. The tool’s domain encourages students to learn about software lifecycles and methods while understanding the benefit of metrics and reflection. 1. 2. 1. PET 1. 0: In 2001, Carnegie Mellon had one of the largest outsourcing firms in the world develop Pet 1. 0. Later the student teams were brought in to do the next release. The initial offerings of the course had the teams follow a Waterfall lifecycle. The faculty decided to use Extreme Programming as the method for the Foundations course because it was an agile method, it had good engineering practices, and it was a safe sandbox environment for engineers to try paired programming since many managers in industry were initially skeptical about its benefits. In 2005, the faculty allowed three of the sixteen teams tried our new curriculum to see if there were any serious issues in the switch, while other thirteen teams continued to follow a start point in 2004. The feedback was extremely positive so in 2006, all teams followed Extreme Programming. For the project plan duration, Waterfall teams needed fifteen weeks to finish their tasks where as Extreme Programming teams were given only thirteen weeks, a 13% reduction in time. 1. 2. 2. PET 1. 1: In 2005, the VP of Engineering advised the three teams that rewriting the code from scratch would be easier than working with the existing code base. Team 30:1 decided to use the latest in Java technologies including Swing and Hibernate. PET 1. 1, the team’s product became the starting point for the students in the following year. 1. 2. 3. PET 1. 2: In 2008, the faculty switched the core technology from Java to Ruby on Rails. Ruby on Rails’ convention over configuration, afforded a lower learning curve for students. For Pet 1. 2, students would build their projects from scratch. 2. Related work Much research has been done as to when to use an agile method and when to use a traditional method. For example, Boehm Turner’s home grounds look at several characteristics, criticality, culture, and dynamism [6]. Our paper aims to extend these limitations to some degree by estimating Waterfall and XP in an academic case study, which provide a substantive ground for researchers before replicating their ideas in industry. Basili [7] presented a framework for analyzing most of the experimental work performed in software engineering. We learned that how to conduct a controlled experiment. Andrew and Nachiappan [8] reported on the results of an empirical study conducted at Microsoft by using an anonymous web-based survey. They found that one third of the study respondents use Agile methodologies to varying degrees and most view it favorably due to improved communication between team members, quick releases and the increased flexibility of agile designs. Their findings that we will consider in our future work is that developers are most worried about scaling Agile to larger projects, and coordinating agile and traditional teams. Our work is closely related to the work by Ming Huo et al [9]. They compared the Waterfall model with agile processes to show how agile methods achieve software quality. They also showed how agile methods attain quality under time pressure and in an unstable requirements environment. They presented a detailed Waterfall model showing its software quality support processes. Other work has only illustrates one or some Agile practices such as pair programming [10]. 3. Experimental methodology Our research was conducted primarily using Glaser’s steps [11] in the constant comparison method of analysis. Step1: Begin collecting data. We collected more than 50 teams’ detailed data during a five year period as Table 1 shows. Table 1. Team building the same project 2004 2005 2005 2006 2007 2008 Method Waterfall Waterfall XP XP XP XP Language Java Java Java Java Java Ruby Project PET1. 0 PET1. 0 PET1. 0 PET1. 1 PET1. 1 PET1. 2 Numbers of Teams 10 13 3 9 6 11 Step2: Look for key issues, recurrent events, or activities in the data that become categories for focus. The approach in software design makes us categorize the data into two distinctive software development methods, namely Waterfall and Extreme Programming. Step3: Collect data that provides many incidents of the categories of focus with an eye to seeing the diversity of the dimensions under the categories. According to Basili[7], we provided some metrics to compare these two categories, Waterfall and XP. Requirements Metrics M1: Numbers of UI screens (ie. mockup) M2: Numbers of use cases (story cards) M3: Pages of Software Requirements Specification (SRS) documents M4: Pages of User Requirements Documents (URD) Design Metric M5: Pages of detailed design documents Implementation Metrics M6: Lines of code M7: Percentage of lines of comments to lines of source code M8: Lines of test cases M9: Ratio of lines of test code to lines of program code Step4: Write about the categories that we are exploring, attempting to describe and account for all the incidents we have in our data while continually searching for new incidents. Step5: Work with the data and emerging model to discover basic social processes and relationships. Step6: Engage in sampling, coding, and writing as the analysis focuses on the core categories. During 2005, there were 13 teams following Waterfall and 3 teams following XP during the same period of time. These three teams, team Absorb, GT11 and 30:1 are interesting teams to examine as we can compare their data against the Waterfall teams doing the exact same project. 4. Experimental results 4. 1. UI screens (M1) and Story cards (M2) comparison These wide ranges can be seen in Table 2 and Table 3 where the standard deviation of the UI mockups is often half the document size. Comparing use cases to story cards in Table 3, we see that the standard deviation for use cases is much lower than the standard deviation for story cards. This is expected since use cases are a higher ceremony document when compared to story cards. Teams might give little consideration to how to represent each feature on a story card whereas a team writing a use case step by step how a user will use the system will spend much more time thinking about the coupling and cohesion of each use case. Table 2. Average numbers and Standard Deviation of mockups Year 004 2005 Absorb GT11 30:1 2006 2007 2008 Average mockups 15. 5 11. 8 17 18 9 15 12. 8 17. 7 Standard Deviation of mockups 6. 6 6. 3 5. 4 3. 1 8. 8 Table 3. Average numbers and Standard Deviation of use cases/story cards Year Average Number Standard Deviation 2004 User cases 18. 7 2005 User cases 18. 9 2. 3 Absorb Story cards 15 1. 6 GT11 Story cards 13 30:1 Story cards 18 2006 Story cards 16. 6 2007 Story cards 18. 3 2008 Story car ds 16. 6 7. 5 6. 8 8. 0 4. 2. Requirement documents (M3M4) Starting with PET 1. 0, Waterfall teams on average add 1. 7 use cases and modified 2. use cases. Teams were given a 28 page System Requirements Specification (SRS) and on averaged finished with a 34 page SRS. XP teams starting with PET 1. 0 were given the same starting documents. Instead of modifying them, the teams created story cards that represented each new feature. Instead of spending time on writing use cases, XP teams started coding sooner. Because XP has an emphasis on low ceremony documents, they had more time to code resulting in an effort savings for the teams. 4. 3. Comparing the size of the detail design documents (M5) There are some insights from Table 4. Waterfall teams using Pet 1. 0 started with a 21 page Detailed Design Document (DDD), which they altered to reflect their new use cases. Waterfall teams typically did not update their design documents at the end of the project. Given the scope of the project, Waterfall teams’ final code matched the original design with respect to new classes. Table 4. Average pages and Standard Deviation of Detail Design Documents Year 2004 2005 Absorb GT11 30:1 2006 2007 2008 Starting Point 21 21 21 21 0 14 14 0 Average DDD 25. 8 31. 1 18 22 14 18. 3 12. 5 9. 5 Standard Deviation 8. 39 7. 48 7. 70 7. 8 5. 19 XP teams increased their design documents with each iteration. Because the XP teams followed Test-Driven Development, they wrote their code and had an emergent design. At the end of each iteration, the teams were asked to update the design document to reflect important design decisions they had made during that iteration. Therefore, the design document serves a different purpose in XP. It is not a template or blueprint for future construction. Instead, it can be a guide for understanding why certain decisions were made. In this regard, it is a biography of the development, ot a plan of action. 4. 4. New lines of source code and comments, Percentage of comments in codes Table 5 shows that Waterfall teams starting with Pet 1. 0 produced lines of code with a wide variance. The two XP teams starting with Pet 1. 0 fell right within the middle of the average. Because instead of producing some documents up front, the XP teams spent a longer time coding, one would expect them to produce more lines of code. The research results also show that XP Teams had a higher percentage of comments in source code. Table 5. Average and Standard Deviation of new lines in code Year Language Average new lines in code Standard Deviation Lines of test codes Ratio of test codes to program code 2004 2005 Absorb GT11 30:1 2006 2007 2008 Java Java Java Java Java Java Java Ruby 9,429 11,910 13,288 14,689 0 9,628 8,572 3,670 7,946 9,851 4,920 5,465 1,507 3378 4164 1380 3186 947 3555 2212 3,255 8% 13% 4% 8% 8% 16% 10% 90% 4. 5. Submitted lines of test codes and ratio of test code to program code The observation of these two metrics in Table 5 shows that the amount of test code written by the Waterfall teams equals the amount of test code written by the XP teams. Initially the faculty thought that Test-Driven Development would increase the amount of testing code, however, given a slow adoption rate of Test-Driven Development, programmers resorted to what was familiar and thus produced similar results. 5. Conclusion In this paper, we observed and presented the data from five years of 50 teams developing the same project each year and the affects of transitioning from Waterfall to Extreme Programming. The characteristics between these two methods were evaluated and compared. Waterfall teams spent more time creating high ceremony documents where as Extreme Programming teams spent more time writing code and documenting their design in their code. Surprisingly, the amount of code and features completed were roughly the same for both methods suggesting that on a three month project with three to four developers it doesn’t matter the method used. It is challenging to conduct this kind of analysis of the data in hindsight. Given that this is not a toy problem, and the freedom teams have in the execution of their projects, setting up this kind of experiment properly in advance is also challenging. . References [1] Sommerville, Software engineering, 8th ed. , New York: Addison-Wesley, Harlow, England, 2006. [2] W. Royce, Managing the Development of Large Software Systems, IEEE WESTCON, Los Angeles, 1970. [3] A. Abran and J. W. Moore, Guide to the software engineering body of knowledge: trial version (version 0. 95) IEEE Computer Society Press, Los Alamito s, CA, USA, 2001. [4] Kent Beck and Cynthia Andres, Extreme programming eXplained: embrace change, Second Edition, MA: Addison-Wesley, 2004. 5] Mike Cohn, Agile estimating and planning, Prentice Hall Professional Technical Reference, Nov 11, 2005. [6] Barry, Boehm and Richard Turner, Balancing Agility and Discipline: A Guide for the Perplexed, Addison Wesley, August 15, 2003. [7] Basil, V. R. , Selby, R. and Hutchens, D. , Experimentation in Software Engineering, IEEE Transactions on Software Engineering (invited paper), July 1986. [8] Andrew Begel and Nachiappan Nagappan, Usage and Perceptions of Agile Software Development in an Industrial Context: An Exploratory Study, MiIEEE Computer Society MSR-TR-2007-09, no. 2007): 10. [9] Ming Huo, June Verner, Muhammad Ali Babar, and Liming Zhu, How does agility ensure quality? , IEEE Seminar Digests 2004, (2004):36. [10] Jan Chong, Robert Plummer, Larry Leifer, Scott R. Klemmer, and George Toye. Pair Programming: When and Why it Works, In P roceedings of Psychology of Programming Interest Group 2005 Workshop, Brighton, UK, June 2005. [11] Glaser, Barney G, Strauss, and Anselm L. , The Discovery of Grounded Theory: Strategies for Qualitative Research, Aldine Publishing Company, Chicago, 1967. How to cite Comparing Extreme Programming and Waterfall Project Results, Papers Comparing Extreme Programming and Waterfall Project Results Free Essays Comparing Extreme Programming and Waterfall Project Results Feng Ji Carnegie Mellon University Silicon Valley Campus Mountain View, CA, 94035 jojojifeng@gmail. com Todd Sedano Carnegie Mellon University Silicon Valley Campus Mountain View, CA, 94035 todd. sedano@sv. We will write a custom essay sample on Comparing Extreme Programming and Waterfall Project Results or any similar topic only for you Order Now cmu. edu Abstract Waterfall and Extreme Programming are two software project methods used for project management. Although there are a number of opinions comparing the two methods regarding how they should be applied, none have used project data to clearly conclude which one is better. In this paper, we present the results of a controlled empirical study conducted at Carnegie Mellon University in Silicon Valley to learn about the effective transition from traditional development to agile development. We conducted a comparison research against these two approaches. Multiple teams were assigned a project; some used Waterfall development, others used Extreme Programming. The purpose of this research is to look at advantages and disadvantages based upon the outcomes, generated artifacts, and metrics produced by the teams. 1. Introduction 1. 1. Agile vs Traditional Since the early 1970s, numerous software managers have explored different ways of software development methods (such as Waterfall model, evolutionary model, spiral model etc. ) those have been developed to accomplish these goals and have been widely used by the software industry [1]. Methodologists often describe the Waterfall method as a stereotypical traditional method whereas they describe Extreme Programming as the stereotypical agile method. The Waterfall model, as the oldest traditional software development method, was cited by Winston W. Royce in 1970 [2]. He divided the software development lifecycle into seven sequential and linear stages: Conception, Initiation, Analysis, Design, Construction, Testing, and Maintenance. The Waterfall model is especially used for large and complex engineering projects. Waterfall’s lasting impression upon software engineering is seen even in the Guide to Software Engineering Body of Knowledge which introduces the first five knowledge areas based upon their sequence in the Waterfall lifecycle even though the Guide does not recommend any particular lifecycle [3]. Although the Waterfall model has been adopted in many large and complex projects, it still has some inherent drawbacks, like inflexibility in the face of changing requirements [1]. If large amounts of project resources have been invested in requirements and design activities, then changes can be very costly later. High ceremony documentation is not necessary in all projects. Agile methods deal well with unstable and volatile requirements by using a number of techniques of which most notable are: low ceremony documents, short iterations, early testing, and customer collaboration. Kent Beck and Cynthia Andres define Extreme Programming 2. 0 with many practices [4], like Pair Programming, Test First Programming, and Continuous Integration and so on. These characteristics enable agile methods to obtain the smallest workable piece of functionality to deliver business value early and continually improving it while adding further functionality throughout the life of the project [5]. 1. 2. PET project background Carnegie Mellon University Silicon Valley students start their masters program with the Foundations of Software Engineering course. This course is team-based, project-based, and mentored. Each team builds The Process Enactment Tool (PET). The user personas are software developers and managers. The tool helps users plan, estimate, and execute a project plan while analyzing historical data. The tool’s domain encourages students to learn about software lifecycles and methods while understanding the benefit of metrics and reflection. 1. 2. 1. PET 1. 0: In 2001, Carnegie Mellon had one of the largest outsourcing firms in the world develop Pet 1. 0. Later the student teams were brought in to do the next release. The initial offerings of the course had the teams follow a Waterfall lifecycle. The faculty decided to use Extreme Programming as the method for the Foundations course because it was an agile method, it had good engineering practices, and it was a safe sandbox environment for engineers to try paired programming since many managers in industry were initially skeptical about its benefits. In 2005, the faculty allowed three of the sixteen teams tried our new curriculum to see if there were any serious issues in the switch, while other thirteen teams continued to follow a start point in 2004. The feedback was extremely positive so in 2006, all teams followed Extreme Programming. For the project plan duration, Waterfall teams needed fifteen weeks to finish their tasks where as Extreme Programming teams were given only thirteen weeks, a 13% reduction in time. 1. 2. 2. PET 1. 1: In 2005, the VP of Engineering advised the three teams that rewriting the code from scratch would be easier than working with the existing code base. Team 30:1 decided to use the latest in Java technologies including Swing and Hibernate. PET 1. 1, the team’s product became the starting point for the students in the following year. 1. 2. 3. PET 1. 2: In 2008, the faculty switched the core technology from Java to Ruby on Rails. Ruby on Rails’ convention over configuration, afforded a lower learning curve for students. For Pet 1. 2, students would build their projects from scratch. 2. Related work Much research has been done as to when to use an agile method and when to use a traditional method. For example, Boehm Turner’s home grounds look at several characteristics, criticality, culture, and dynamism [6]. Our paper aims to extend these limitations to some degree by estimating Waterfall and XP in an academic case study, which provide a substantive ground for researchers before replicating their ideas in industry. Basili [7] presented a framework for analyzing most of the experimental work performed in software engineering. We learned that how to conduct a controlled experiment. Andrew and Nachiappan [8] reported on the results of an empirical study conducted at Microsoft by using an anonymous web-based survey. They found that one third of the study respondents use Agile methodologies to varying degrees and most view it favorably due to improved communication between team members, quick releases and the increased flexibility of agile designs. Their findings that we will consider in our future work is that developers are most worried about scaling Agile to larger projects, and coordinating agile and traditional teams. Our work is closely related to the work by Ming Huo et al [9]. They compared the Waterfall model with agile processes to show how agile methods achieve software quality. They also showed how agile methods attain quality under time pressure and in an unstable requirements environment. They presented a detailed Waterfall model showing its software quality support processes. Other work has only illustrates one or some Agile practices such as pair programming [10]. 3. Experimental methodology Our research was conducted primarily using Glaser’s steps [11] in the constant comparison method of analysis. Step1: Begin collecting data. We collected more than 50 teams’ detailed data during a five year period as Table 1 shows. Table 1. Team building the same project 2004 2005 2005 2006 2007 2008 Method Waterfall Waterfall XP XP XP XP Language Java Java Java Java Java Ruby Project PET1. 0 PET1. 0 PET1. 0 PET1. 1 PET1. 1 PET1. 2 Numbers of Teams 10 13 3 9 6 11 Step2: Look for key issues, recurrent events, or activities in the data that become categories for focus. The approach in software design makes us categorize the data into two distinctive software development methods, namely Waterfall and Extreme Programming. Step3: Collect data that provides many incidents of the categories of focus with an eye to seeing the diversity of the dimensions under the categories. According to Basili[7], we provided some metrics to compare these two categories, Waterfall and XP. Requirements Metrics M1: Numbers of UI screens (ie. mockup) M2: Numbers of use cases (story cards) M3: Pages of Software Requirements Specification (SRS) documents M4: Pages of User Requirements Documents (URD) Design Metric M5: Pages of detailed design documents Implementation Metrics M6: Lines of code M7: Percentage of lines of comments to lines of source code M8: Lines of test cases M9: Ratio of lines of test code to lines of program code Step4: Write about the categories that we are exploring, attempting to describe and account for all the incidents we have in our data while continually searching for new incidents. Step5: Work with the data and emerging model to discover basic social processes and relationships. Step6: Engage in sampling, coding, and writing as the analysis focuses on the core categories. During 2005, there were 13 teams following Waterfall and 3 teams following XP during the same period of time. These three teams, team Absorb, GT11 and 30:1 are interesting teams to examine as we can compare their data against the Waterfall teams doing the exact same project. 4. Experimental results 4. 1. UI screens (M1) and Story cards (M2) comparison These wide ranges can be seen in Table 2 and Table 3 where the standard deviation of the UI mockups is often half the document size. Comparing use cases to story cards in Table 3, we see that the standard deviation for use cases is much lower than the standard deviation for story cards. This is expected since use cases are a higher ceremony document when compared to story cards. Teams might give little consideration to how to represent each feature on a story card whereas a team writing a use case step by step how a user will use the system will spend much more time thinking about the coupling and cohesion of each use case. Table 2. Average numbers and Standard Deviation of mockups Year 004 2005 Absorb GT11 30:1 2006 2007 2008 Average mockups 15. 5 11. 8 17 18 9 15 12. 8 17. 7 Standard Deviation of mockups 6. 6 6. 3 5. 4 3. 1 8. 8 Table 3. Average numbers and Standard Deviation of use cases/story cards Year Average Number Standard Deviation 2004 User cases 18. 7 2005 User cases 18. 9 2. 3 Absorb Story cards 15 1. 6 GT11 Story cards 13 30:1 Story cards 18 2006 Story cards 16. 6 2007 Story cards 18. 3 2008 Story car ds 16. 6 7. 5 6. 8 8. 0 4. 2. Requirement documents (M3M4) Starting with PET 1. 0, Waterfall teams on average add 1. 7 use cases and modified 2. use cases. Teams were given a 28 page System Requirements Specification (SRS) and on averaged finished with a 34 page SRS. XP teams starting with PET 1. 0 were given the same starting documents. Instead of modifying them, the teams created story cards that represented each new feature. Instead of spending time on writing use cases, XP teams started coding sooner. Because XP has an emphasis on low ceremony documents, they had more time to code resulting in an effort savings for the teams. 4. 3. Comparing the size of the detail design documents (M5) There are some insights from Table 4. Waterfall teams using Pet 1. 0 started with a 21 page Detailed Design Document (DDD), which they altered to reflect their new use cases. Waterfall teams typically did not update their design documents at the end of the project. Given the scope of the project, Waterfall teams’ final code matched the original design with respect to new classes. Table 4. Average pages and Standard Deviation of Detail Design Documents Year 2004 2005 Absorb GT11 30:1 2006 2007 2008 Starting Point 21 21 21 21 0 14 14 0 Average DDD 25. 8 31. 1 18 22 14 18. 3 12. 5 9. 5 Standard Deviation 8. 39 7. 48 7. 70 7. 8 5. 19 XP teams increased their design documents with each iteration. Because the XP teams followed Test-Driven Development, they wrote their code and had an emergent design. At the end of each iteration, the teams were asked to update the design document to reflect important design decisions they had made during that iteration. Therefore, the design document serves a different purpose in XP. It is not a template or blueprint for future construction. Instead, it can be a guide for understanding why certain decisions were made. In this regard, it is a biography of the development, ot a plan of action. 4. 4. New lines of source code and comments, Percentage of comments in codes Table 5 shows that Waterfall teams starting with Pet 1. 0 produced lines of code with a wide variance. The two XP teams starting with Pet 1. 0 fell right within the middle of the average. Because instead of producing some documents up front, the XP teams spent a longer time coding, one would expect them to produce more lines of code. The research results also show that XP Teams had a higher percentage of comments in source code. Table 5. Average and Standard Deviation of new lines in code Year Language Average new lines in code Standard Deviation Lines of test codes Ratio of test codes to program code 2004 2005 Absorb GT11 30:1 2006 2007 2008 Java Java Java Java Java Java Java Ruby 9,429 11,910 13,288 14,689 0 9,628 8,572 3,670 7,946 9,851 4,920 5,465 1,507 3378 4164 1380 3186 947 3555 2212 3,255 8% 13% 4% 8% 8% 16% 10% 90% 4. 5. Submitted lines of test codes and ratio of test code to program code The observation of these two metrics in Table 5 shows that the amount of test code written by the Waterfall teams equals the amount of test code written by the XP teams. Initially the faculty thought that Test-Driven Development would increase the amount of testing code, however, given a slow adoption rate of Test-Driven Development, programmers resorted to what was familiar and thus produced similar results. 5. Conclusion In this paper, we observed and presented the data from five years of 50 teams developing the same project each year and the affects of transitioning from Waterfall to Extreme Programming. The characteristics between these two methods were evaluated and compared. Waterfall teams spent more time creating high ceremony documents where as Extreme Programming teams spent more time writing code and documenting their design in their code. Surprisingly, the amount of code and features completed were roughly the same for both methods suggesting that on a three month project with three to four developers it doesn’t matter the method used. It is challenging to conduct this kind of analysis of the data in hindsight. Given that this is not a toy problem, and the freedom teams have in the execution of their projects, setting up this kind of experiment properly in advance is also challenging. . References [1] Sommerville, Software engineering, 8th ed. , New York: Addison-Wesley, Harlow, England, 2006. [2] W. Royce, Managing the Development of Large Software Systems, IEEE WESTCON, Los Angeles, 1970. [3] A. Abran and J. W. Moore, Guide to the software engineering body of knowledge: trial version (version 0. 95) IEEE Computer Society Press, Los Alamito s, CA, USA, 2001. [4] Kent Beck and Cynthia Andres, Extreme programming eXplained: embrace change, Second Edition, MA: Addison-Wesley, 2004. 5] Mike Cohn, Agile estimating and planning, Prentice Hall Professional Technical Reference, Nov 11, 2005. [6] Barry, Boehm and Richard Turner, Balancing Agility and Discipline: A Guide for the Perplexed, Addison Wesley, August 15, 2003. [7] Basil, V. R. , Selby, R. and Hutchens, D. , Experimentation in Software Engineering, IEEE Transactions on Software Engineering (invited paper), July 1986. [8] Andrew Begel and Nachiappan Nagappan, Usage and Perceptions of Agile Software Development in an Industrial Context: An Exploratory Study, MiIEEE Computer Society MSR-TR-2007-09, no. 2007): 10. [9] Ming Huo, June Verner, Muhammad Ali Babar, and Liming Zhu, How does agility ensure quality? , IEEE Seminar Digests 2004, (2004):36. [10] Jan Chong, Robert Plummer, Larry Leifer, Scott R. Klemmer, and George Toye. Pair Programming: When and Why it Works, In P roceedings of Psychology of Programming Interest Group 2005 Workshop, Brighton, UK, June 2005. [11] Glaser, Barney G, Strauss, and Anselm L. , The Discovery of Grounded Theory: Strategies for Qualitative Research, Aldine Publishing Company, Chicago, 1967. How to cite Comparing Extreme Programming and Waterfall Project Results, Essay examples

Contemporary Strategic Analysis

Question: Describe how the strategy has been specifically tailored to overcome the difficulty that the firm is experiencing. Answer: Introduction: Target Australia constitutes one of the majors in the domain of retail business which spans over three hundred communities across the nation with its units located conveniently in regional as well as metropolitan areas. Target Group offers the consumers a wide range of products, including the likes of apparels, electrical appliances and games and entertainment. The entity was formed by George Lindsay and Alex McKenzie in Geelong located in Victoria as a result of partnership (target.com.au, 2016). The firm initiated transition model in the ranks of the organization through the means of the refurbishment of old units which aimed to achieve increase as far as profitability is concerned. The firm strives to operate as an ethical, sustainable and responsible business. Owing to complexities of various degrees the firm is in doldrums in recent past. The organization has had a tough time in the light of harsh economic conditions along with poor economic confidence. The firm is battling with poor retailer relationship and other business intricacy. The poor economic climate and environment have resulted in the firm's declining fortunes as far as earnings are concerned. Furthermore, with the emergence of growing competition from the retailers, the firm is lagging about business interface (Huang Zhu, 2016). Detailed discussion: In recent past, the firm is experiencing troubles and plight while maintaining its operations and also falling apart in the light of increasingly competitive global environment which witness is growing the number of retailers in an expansion phase in Australia and further emphasis on online retaining business. The following detailed analysis highlights key problems, strategies for overcoming the problems, and measures that define the efficacy of the strategies involved in it. Existing predicament of Target Group: The biggest problem that the firm is battling to strive with is the brisk decline in profits of the firm. Targets profit has suffered a hit by thirty-two percent over the two years and further sixty percent since then. The whole retail domain has been in a perilous situation over the last quarter. The investors have lost faith in the retail shares over the past six months. They have been in anticipation for a recovery with the Reserve Bank pushing interest rates to low levels (Cao et al., 2015). The industrial analysts opine that the trends of sales have slowed by 2 percent to three percent in the last three months or so. The trouble of Target group exerts a contagion effect. As a matter of fact, the firm would require discounting heavily to get rid of the stocks. On the other hand, consumers are also not in the correct frame of mind to buy products of Target in particular due to its stocks hitting the rock bottom in the market. The cost of the firm regarding production is also too h igh. This is resultant of hike in international labor and transportation costs in the industry. This has had a drastic effect on the firm since most of the products and solutions are manufactured in Asian markets. Earlier, with increase of cotton cost the firms apparel profitability experienced a severe hit in 2011 (Chang, 2014). Identification of Problems: At the outset, it is worth mentioning that the Australian Retail domain has witnessed the downward trend post-global financial meltdown. The sectors share in the Australian GDP has been on a downward trend. Having said that, the recent changes in the Australian economy in keeping with the consumer sentiments and behavior had affected a structural shift in the industry (Du et al., 2016). The key problems or concerns identified in this context are mainly, competitors having larger distribution areas. The players mainly, Wal-Mart owns a larger amount of stores and units in comparison to that of Target Australia. The inflation rate, on the other hand, is higher due to fall of the economy in preceding years. The competitors have gone about by the elimination of the negative effect by offering prices for identical products and items. There have been reports of security breaches wherein the personal information of the consumers has been hacked thereby conferring bad name to the organization . Furthermore, the mergers and acquisition of various companies across the globe in the similar domain have resulted in Targets diminutive fortunes. The firm also lacks diversification regarding products and items which the players have an edge over Target Corporation (Hattersley, Isaacs Burch, 2013). Design of strategy for overcoming of problems: As mentioned earlier, that Target Group has experienced a decline in profitability index. The profits have declined by 32 percent in the preceding years before Mr. Machin joining the group for revitalizing the firm. But things turned worse with profits declining to 63 percent since his joining of the group. Hence, it is imperative for the firm to devise strategies that would improve the condition of the business. The various strategies designed to overcome the problems are laid below. Online Business operation: In the present context, technology plays a major role in business which cannot be undermined to any extent. In the quest of augmenting the firm's prospects, Target Group may implement an online business model to attract technologically sound consumer base. The online business model of Target aspires for improvement of sales figures and repositioning itself in the retail domain. Other move includes round the clock operations, and better service delivery to the customers (Wardle, 2015). Sale of products at a first price: The firm has been selling its products and services to the consumers at discounted rates. This may have reaped benefits primarily. But with the passage of time, this business strategy or approach resulted in the decline of the firm's performance concerning sales revenue and other indicators. Hence, to recover the lost ground or improvement of profit margins of the organization and overall performance of the firm, Target Group needs to revamp its selling strategy. The firm has changed the pricing strategy by the sale of products at the first price instead of discounted prices. This tactic should facilitate the organization for the avoidance of soaring stock clearances and also strengthen the company in the form of revenue generation. This strategic change would facilitate the firm to improve its business state of affairs by improvement of sales and reestablishment of the firms presence in Australian retail domain through differentiation (Tennent Loc kie, 2012). Additional services: Target Group has initiated strategic change by the implementation of additional services in its units. The services gamut has been broadened by the company for its consumers and thereby improving service delivery quality to the consumers. The services rendered to the in-house customers are efficient and faster than earlier. This has empowered the consumers to have a favorable outlook of the organization in general. Additionally, the structure of the store has been refurbished which augments the ambiance and additional facilities of the store by initiation of in-house coffee shops that has helped in the enhancement of the brand in the psyche of the consumers, by and large (Kamal et al., 2015). Global trends: Target is one of the prominent retailers in the Australian retail market that provides services to the customers apparels, home wares, and general merchandise. As a part of strategy change, Target is providing customers more assortment of various products or merchandises that are in line with the global trends and demands of the customers. Besides, Target is planning to hire in-house designers that would design apparels exclusively for the customers of Target by considering the preferences of their customers and also the international trends. This would improve the brand loyalty among the customers and also the profits for the company. Effectiveness of strategies: The discussion above of the strategy implemented by the organization, it is imperative to decipher the effectiveness of the approaches adopted by Target Group. Initially, it was observed that the firm relied on adoption of the new business model since the present day state of affairs aids any organizations to arrest the attention of the consumers (Schneider, 2015). It may be stated that the online business model of target Group would facilitate the consumers to select products and services from an array of products, services, and solutions. Hence, it may be deduced that subscribing to the suggested strategies or for that matter tactics, would make buying experience of the consumers more refined. Also, the modified business policies would necessitate the corporation for revival of existent tactics thereby helping the organization to attain profitability and hence these tactics are justified enough (Higgins, Milne Van Gramberg, 2015). Explanation of success of Strategy: In the light of competitiveness in the industry, organizations are required to stock the emerging changes in the retail domain of the country. It is mentioned in the above segment that the strategies implemented or made into use in the ranks of the organization have drawn results much to the benefit of the corporation by and large (Sutton-Brady, Kamvounias Taylor, 2015). Firstly, Target Group had reduced its dependence of offering consumers at discount rates. With the change in consumer tastes and preference, the consumers are more likely to have a propensity of buying premium products at considerable price rates rather than mediocre products and solutions. Value creation is one of the strategies that helped the firm to gain an advantage over other players in the domain. Target Group had created value for its consumer base since they offer high-end merchandises in the light of global and emergent trends and perspectives (Merrilees Miller, 2015). Measures and indicators of strategy: The development in the organization is evident from the financial dynamics. By the means of application or implementation of strategies, Target Groups financial worth or reading reveal a positive effect. The factual reality is Targets stock prices soared from the period of February 2014 till 2015, from 55.06 to 75.87 (Wardle, 2015). Here, it is important to learn that during the same time frame, Wal-Mart, which is a major player in the domain, witnessed stocks decrease from 72.81 to 86.18. Having said that, Target Groups worth increased around 6 USD more than that of Wal-Marts both of these two organizations are on the same plane and assume a decent market position. On the other hand, Targets sales revenue stayed reasonably the same during the same time frame, decreasing by merely less than one percent. The Target Group seems to perform well regarding Cost of Goods Sold and Sales ratio, which signifies that the firm stays ahead of players in the business environment (Chimhundu, McNei ll Hamlin, 2015). Rationale of choice: The rationality of usage of such kind of indicators or measures lies in the evaluation of the financial position of the firm. The Key Performance Indicators or KPI in this perspective is a financial consideration or stocks of the firm in keeping with the amplification of consumer sales and broadening of customer baseline (Knight, 2016). It is worthwhile to note that financials readings are apparently unlike to that of consumer enhancement. But a further in-depth analysis reveals that strengthening of consumers only adds to augmentation of the financial health of the performance of Target Group. Therefore, a review of all the strategies and KPIs, it may be construed that the choice of aforementioned strategies is acceptable enough and that it is likely that the entity would invigorate through the means of the same strategies and approaches (Knight, 2015). Conclusion: The overall analysis and in-depth investigation in the contemporary strategic study of Target Group put forth about nuances of how marketing strategies and developments aids in bettering of a firms operation and performance. The earlier segment mentions about predicaments or shortcomings in the firms operations. The further study reveals about creation and formulation of strategies which intends to resolve the crisis of the firm that it has been subject to in present context. Target initiated refurbishment process in its ranks by revamping the marketing spectrum of the organization, in general. Various methods have been discussed in coherence which highlights the need for alteration of existing strategies to the innovation of policies and thereby relevance of the same in the given perspective. In fine, it may be concluded that Target Group is on the verge of regaining its stature by the means of application of diverse strategies and policies applied in the given proposition. References: Cao, Y., Zhao, K., Yang, J., Xiong, W. (2015). Constructing the integrated strategic performance indicator system for manufacturing companies. International Journal of Production Research, 53(13), 4102-4116. Chang, W. J. (2014). Market orientation and business-to-business (B2B): a meta-analysis perspective. International Journal of Services Technology and Management, 20(1-3), 123-148. Chimhundu, R., McNeill, L. S., Hamlin, R. P. (2015). Manufacturer and retailer brands: is strategic coexistence the norm?. Australasian Marketing Journal (AMJ), 23(1), 49-60. Du, F., Yang, F., Liang, L., Yang, M. (2016). Do service providers adopting market segmentation need cooperation with third parties? An application to hotels. International Journal of Contemporary Hospitality Management, 28(1). Hattersley, L., Isaacs, B., Burch, D. (2013). Supermarket power, own-labels, and manufacturer counterstrategies: international relations of cooperation and competition in the fruit canning industry. Agriculture and human values, 30(2), 225-233. Higgins, C., Milne, M. J., Van Gramberg, B. (2015). The uptake of sustainability reporting in Australia. Journal of Business Ethics, 129(2), 445-468. Huang, X., Zhu, Y. (2016). Strategic Entry Considerations and Their Impact on Investment Performance in the Australian Mining Industry. In Managing Chinese Outward Foreign Direct Investment (pp. 13-43). Palgrave Macmillan UK. Kamal, O., Brown, D., Sivabalan, P., Sundin, H. (2015). Accounting information and shifting stakeholder salience: an industry level approach. Qualitative Research in Accounting Management, 12(2), 172-200.