Binary/Trinary/etc Trees in LaTeX

For all you LaTeX fans out there, thought you might find this interesting. There is a package out there that lets you represent a decision tree in LaTeX using relatively simple code. You can download it here.

Now for a quick example.
\Tree [.A [.true [.B [.true $+$ ] [.false $-$ ] ] ] [.false [.C [.true [.D [.true $+$ ] [.false $-$ ] ] ] [.false $-$ ] ] ] ]

Turns into the image at the left. The only limitations are that each node can have no more than 5 childeren and the whole tree must be less than 20 levels deep.

POM2

In 2008, Dr. Menzies authored a paper titled "Using Simulation to Investigate Requirements Prioritization Strategies". This paper showed the effects of project and requirement dynamism on the software development processes. In the Spring of 2008, he tasked his Artificial Intelligence class to expand on this model.

Thus, POM2 was born. One of the main differences between POM and POM2 is that POM focused on small teams and small projects. POM2 built its model around the 5 different branches proposed by Dr. Turner and Dr. Boehm. This varied the project size as well as introducing other factors.

• Personnel - The number skill level of the staff
  
• Dynamism - The amount of project change
• Criticality - The impact of failure after deployment
• Culture - How accepting the staff is of change
• Size - The total number of staff across all teams
  

POM2 implimented all of the branches excluding Personnel. With the information available to us, Personnel was a wash. Better people cost more and produced more. Less talented people cost less and produced less. This lead to a near zero effect.

POM2 was then put through a Monte Carlo simulator while a KEYS search engine isolated the interesting areas in the simulator.

The main discovery is that Agile development methods performed as well as or better than Plan based development methods in all areas, especially excelling in dynamic environments. Further research is needed into the Personell and Criticality branches of POM2 to fully flesh out the model.

POM2 was accepted as a short paper to ASE'09. The full version of the paper can be found here, and the short version can be found here.

Bryan Lemon

Bryan is currently a Computer Science Master's student at West Virginia University. He graduated with his BS in Computer Science from Bluefield State College in spring of 2008.

He, along with a team of other students at Bluefield State College competed in The Intelligent Ground Vehicle competition in the summer of 2008. They took first place in the Autonomous Challenge and 4th overall.

In the Fall of 2009, he, his advisor, and his classmates submitted a paper to ASE'09 detailing a software process development model. It will be featured as a short paper in November's conference. He is currently developing a Decision Tree based clusterer called Ripple Down Rules.

Bryan plans on going on to the PhD program upon completing his Masters degree. Once all is said and done, maybe he will finally have the time to develop a hobby.

(Short) Paper accepted to ASE'09

Assessing the Relative Merits of Agile vs Traditional Software Development.

Bryan Lemon, Aaron Riesbeck, Tim Menzies, Justin Price, Joseph D’Alessandro, Rikard Carlsson, Tomi Prifiti, Fayola Peters, Hiuhua Lu, Dan Port

We implemented Boehm-Turner’s model of agile and plan-based software development. That tool is augmented with an AI search engine to find the key factors that predict for the success of agile or traditional plan-based software developments. According to our simulations and AI search engine: (1) in no case did agile methods perform worse than plan-based approaches; (2) in some cases, agile performed best. Hence, we recommend that the default development practice for organizations be an agile method. The simplicity of this style of analysis begs the question: why is so much time wasted on evidence-less debates on software process when a simple combination of simulation plus automatic search can mature the dialogue much faster?