A code smell is a code pattern that might indicate a code or design problem, which makes the application code hard to evolve and maintain. Automatic detection of code smells has been studied to help users find which parts of their application codes should be refactored. However, code smells have not been defined in a formal manner. Moreover, existing detection tools are designed mainly for object-oriented applications, but rarely provided for high performance computing (HPC) applications. HPC applications are usually optimized for a particular platform to achieve a high performance, and hence have special code smells called platform-specific code smells (PSCSs). The purpose of this work is to develop a code smell alert system to help users find PSCSs of HPC applications to improve the performance portability across different platforms. This paper presents a PSCS alert system that is based on an abstract syntax tree (AST) and XML. Code patterns of PSCSs are defined in a formal way using the AST information represented in XML. XML Path Language (XPath) is used to describe those patterns. A database is built to store the transformation recipes written in XSLT files for eliminating detected PSCSs. The recall and precision evaluation results obtained by using real applications show that the proposed system can detect potential PSCSs accurately. The evaluation on performance portability of real applications demonstrates that eliminating PSCSs leads to significant performance International Journal of Networking and Computing changes and therefore the code portions with detected PSCSs have to be refactored to improve the performance portability across multiple platforms.
In the work of Wang et al. REF , a new methodology was developed to detect Platform-Specific Code Smells (PSCSs) in High Performance Computing applications using Abstract Syntax Tree (AST) and XML.
Identification and Elimination of Platform-Specific Code Smells in High Performance Computing Applications
Int. J. Netw. Comput.
Int. J. Netw. Comput.
Computer Science