Perspectives on search strategies in automated test input generation  

作　　者：Yang CAO Yanyan JIANG Chang XU Jun MA Xiaoxing MA 

机构地区：[1]State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China [2]Department of Computer Science and Technology,Nanjing University,Nanjing 210023,China

出　　处：《Frontiers of Computer Science》2020年第3期217-219,共3页中国计算机科学前沿（英文版）

基　　金：supported in part by National Key R&D Program(#2017YFB1001801);the National Natural Science Foundation of China(Grant Nos.#61690204 and#61802165).

摘　　要：1 Introduction Automatically generating a limited number of high-quality inputs to reveal bugs,crashes,and hangs becomes a central problem in software testing research[1].The two most extensively studied approaches to automated test input generation are fuzzing[2]and dynamic symbolic execution[3,4](DSE).Both fuzzing and DSE can effectively generate structural test inputs for non-trivial programs without human aids,and are extensively studied in existing literatures[3-5].However,they are also considerably different.To further understand in what sense they are similar or different,and to understand the strengths and limitations of both techniques,we raise the following two questions:(Q1)How do existing fuzzing and DSE techniques model the input space and manage the search procedure?

关 键 词：AUTOMATED GENERATING considerably 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]