While software development productivity has grown rapidly, the weight values assigned to count standard Function Point (FP) created at IBM twenty-five years ago have never been updated. This obsolescence raises critical questions about the validity of the weight values; it also creates other problems such as ambiguous classification, crisp boundary, as well as subjective and locally defined weight values. All of these challenges reveal the need to calibrate FP in order to reflect both the specific software application context and the trend of todays software development techniques more accurately. We have created a FP calibration model that incorporates the learning ability of neural networks as well as the capability of capturing human knowledge using fuzzy logic. The empirical validation using ISBSG Data Repository (release 8) shows an average improvement of 22% in the accuracy of software effort estimations with the new calibration.
Arts festivals are common in America. One successful festival held in Shreveport, Louisiana is the Red River Revel Arts Festival. The Revel features numerous artists, in addition to musical entertainment. Though the Revel charges admission, its primary revenue source is concession sales. Anecdotal evidence argues that concession sales are dependent upon many things, such as: the temperature, rain, and admission fee. This paper estimates a demand function for concession sales at the Revel between 1995 and 2004. This work has policy implications for those running the Revel, while also being of interest to the economist conducting research on similar topics.
Function Points is an important and well-accepted software size metric. However, it is absolutely essential to accurately calibrate Function Point (FP), whose aims are to fit specific software application, to reflect software industry trend, and to improve cost estimation. Neuro-Fuzzy is a technique that incorporates the learning ability from neural network and the ability to capture human knowledge from fuzzy logic. We developed a Neuro-Fuzzy model to calibrate Function Points. The empirical validation using ISBSG data repository Release 8 shows a 22% improvement in software effort estimation after calibration using Neuro-Fuzzy technique.
