- The Implementation of EXtreme Programming XP in a Bioinformatics Class
The Implementation of EXtreme Programming XP in a Bioinformatics Class
One of the things that developers have to be when they use eXtreme Programming or XP, is bold, developers need to learn to be bold, if they want to use XP. They can’t be afraid, because XP was developed, in order to help, solve the problems, which have plagued software development, for many years.
The values of simplicity, communication, feedback and courage are all the basis, of the principles of XP. A good example of XP would be this, imagine if a country is trying to build, the most expensive, time consuming, state of the art missile, in order to bomb and destroy an enemy post. A lot of things could go wrong, before and after that missile is developed; because before, during and after, the development of the missile, things could happen, such as the enemy may fire back and attack. Also the enemy may change strategies, and leave that post, by the time the missile is developed, also the developed missile, may miss the enemy post all together. Finally the missile may not even be developed, due to lack of funds and scope creeps.
In XP the missile would have been developed fast, and at a cheaper rate, because if those same problems arise, the cheaper missile can be developed again and fired at the enemy again and again, until the target is hit and destroyed, avoiding the pitfalls encountered during the first set of development. In XP, the most important functionalities of a product are delivered first, more functionalities can be then delivered once the most significant parts of a product, have been delivered. When the missile has to be built over and over again, this process in known as refactoring, which is a scary thing, for a developer because he/she has to change their code, to solve a new problem.
The chosen article this week, analyzes the Extreme Programming (EP) methodology, used in a bioinformatics class. Switching roles between labs, individual accountability, working in pairs and partner interdependence, were all parts of the EP version of the bioinformatics class. During each week of the class, new pairing were created at random; students gave their feedback, about their partners, the technology and the materials.
Statistical results, with a number of subjects, were provided after Kelley, Alger and Deutschman (2009), used a Repeated Measure-ANOVA or RM-ANOVA statistical design. The researches, found that between the undergraduate, the graduate students and students with experience with the technology, didn’t show any difference in satisfaction of each other or the technology.
The more difficult computer programming parts of the course, was rated higher, and the web-based exercise, had a low score, from the participants. In order to enhance student satisfaction and increase the usage of workstation computer, extreme programming can and should easily, be implemented in a bioinformatics class, according to the article. The only drawback of the study, was the lack of a control or comparison group; due to the lack of people in the class, the study was hindered, because the researchers believed that insufficient data, would have resulted from the study, if the class was divided into smaller groups. Therefore, more research into this area, using a large number of students, should be done, before implementing the XP programming methodology to a Bioinformatics class.
Thank you, for reading this article!!!
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Hi every one, I obtained a bachelor's degree in Bioinformatics back in 2006, from Claflin University, after I received my bachelor's degree, I gained full time employment as a software engineer at a Video Relay Service company, maintaining databases and developing software for a new developed device called the VPAD.
I worked at that company for two years, then I became a web developer, and worked for a magazine for three years. After that job, I worked as a Drupal web developer, as a subcontractor for the NIH, for a year and then left the job to go back to school.
Collaboratively administrate empowered markets via plug-and-play networks. Dynamically procrastinate B2C users after installed base benefits. Dramatically visualize customer directed convergence without
Collaboratively administrate empowered markets via plug-and-play networks. Dynamically procrastinate B2C users after installed base benefits. Dramatically visualize customer directed convergence without revolutionary ROI.