Exploiting tree structures for classifying programs by functionalities

Abstract

Analyzing source code to solve software engineering problems such as fault prediction, cost, and effort estimation always receives attention of researchers as well as companies. The traditional approaches are based on machine learning, and software metrics obtained by computing standard measures of software projects. However, these methods have faced many challenges due to limitations of using software metrics which were not enough to capture the complexity of programs. The aim of this paper is to apply several natural language processing techniques, which deal with software engineering problems by exploring information of programs' abstract syntax trees (ASTs) instead of software metrics. To speed up computational time, we propose a pruning tree technique to eliminate redundant branches of ASTs. In addition, the k-Nearest Neighbor (kNN) algorithm was adopted to compare with other methods whereby the distance between programs is measured by using the tree edit distance (TED) and the Levenshtein distance. These algorithms are evaluated based on the performance of solving 104-label program classification problem. The experiments show that due to the use of appropriate data structures although kNN is a simple machine learning algorithm, the classifiers achieve the promising results.