Software defect prediction

Abstract

Defects are common in software systems and can potentially cause various problems to software users, as software systems are getting larger and more complex, more defects can occur. Despite meticulous planning, well documentation and proper process control during software development, occurrences of a certain defects are inevitable. Many software development activities are performed by individuals, which may lead to different software defects over the development to occur, causing disappointments in the not-so distant future. Software defect prediction identifies the modules that are defect-prone and require extensive testing by capturing the syntax and different levels of semantics of source code. Software defect prediction plays an important role in improving software quality and it helps to reduce cost, time, and resources. Different methods have been developed to quickly predict the most likely locations of defects in large code bases. Most of them focus on designing features (e.g. complexity metrics) that correlate with potentially defective code. Those approaches however do not sufficiently capture the syntax and different levels of semantics of source code. However, to overcome this issue, a new technique has been introduced using source code to generate an abstract syntax tree to capture syntax and semantics. In addition to powerful deep learning techniques. This paper used recurrent neural networks and convolutional neural networks to achieve an accuracy of 78%, and 91% respectively.