Classification of machine learning engines using latent semantic indexing

With the huge increase of software functionalities, sizes and application domain, the difficulty of categorizing and classifying software for information retrieval and maintenance purposes is on demand.This work includes the use of Latent Semantic Indexing (LSI) in classifying neural network and k-nearest neighborhood source code programs. Functional descriptors of each program are identified by extracting terms contained in the source code.In addition, information on where the terms are extracted from is also incorporated in the LSI.Based on the undertaken experiment, the LSI classifier is noted to generate a higher precision and recall compared to the C4.5 algorithm as provided in the Weka tool

Software Classification Using Structure-Based Descriptors

With the huge increase of software functionalities, sizes and application domain, the difficulty of categorizing and classifying software packages for reuse and maintenance purposes is on demand. Building automatic classification mechanism will help to save the budget, time, and the efforts of the organizations, especially the administrators of software repositories. This work includes the use of structure information contained in source code programs to automate program classification. Three software metrics namely; LOC, MVG and WMCl have been extracted from programs of category board and puzzle obtained from SourceForge.net. A total of 2800 programs have been used during the training process while two different datasets of size (28) were used for testing. Based on the undertaken experiment, the IBK algorithm is noted to generate the highest classification accuracy (74.8%) compared to several other algorithms provided in the Weka tool. The study also shows that board programs are written in different structure compared to the puzzle programs. Hence, showing that structure information can be used to classify programs into application domain