C-CORE:Clustering by Code Representation to Prioritize Test Cases in Compiler Testing  

作　　者：Wei Zhou Xincong Jiang Chuan Qin 

机构地区：[1]School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai,200240,China [2]Hikvision Research Institute,Hangzhou Hikvision Digital Technology Co.,Ltd.,Hangzhou,310051,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第5期2069-2093,共25页工程与科学中的计算机建模（英文）

摘　　要：Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-COR

关 键 词：Compiler testing test case prioritization code representation 

分 类 号：TP314[自动化与计算机技术—计算机软件与理论]