Random Search and Code Similarity-Based Automatic Program Repair  

作　　者：曹鹤玲 刘方正 石建树 楚永贺 邓淼磊 CAO Heling;LIU Fangzheng;SHI Jianshu;CHU Yonghe;DENG Miaolei(College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China;Henan International Joint Laboratory of Grain Information Processing,Henan University of Technology,Zhengzhou 450001,China)

机构地区：[1]College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China [2]Henan International Joint Laboratory of Grain Information Processing,Henan University of Technology,Zhengzhou 450001,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2023年第6期738-752,共15页上海交通大学学报（英文版）

基　　金：the Cultivation Programme for Young Backbone Teachers in Henan University of Technology,the Key Scientific Research Project of Colleges and Universities in Henan Province(No.22A520024);the Major Public Welfare Project of Henan Province(No.201300311200);the National Natural Science Foundation of China(Nos.61602154 and 61340037)。

摘　　要：In recent years,automatic program repair approaches have developed rapidly in the field of software engineering.However,the existing program repair techniques based on genetic programming suffer from requiring verification of a large number of candidate patches,which consume a lot of computational resources.In this paper,we propose a random search and code similarity based automatic program repair(RSCSRepair).First,to reduce the verification computation effort for candidate patches,we introduce test filtering to reduce the number of test cases and use test case prioritization techniques to reconstruct a new set of test cases.Second,we use a combination of code similarity and random search for patch generation.Finally,we use a patch overfitting detection method to improve the quality of patches.In order to verify the performance of our approach,we conducted the experiments on the Defects4J benchmark.The experimental results show that RSCSRepair correctly repairs up to 54 bugs,with improvements of 14.3%,8.5%,14.3%and 10.3%for our approach compared with jKali,Nopol,CapGen and Sim Fix,respectively.

关 键 词：program repair random search test case prioritization overfitting detection 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]