MoFuzz: A Fuzzer Suite for Testing Model-Driven Software Engineering Tools.

模糊测试 计算机科学 一套 软件工程 测试套件 程序设计语言 象征性执行 随机测试 软件测试 代码覆盖率 一致性(知识库) 测试用例 软件 机器学习 人工智能 回归分析 考古 历史
作者
Hoang Lam Nguyen,Nebras Nassar,Timo Kehrer,Lars Grunske
出处
期刊:Software engineering [Science Publishing Group]
卷期号:: 81-82 被引量:2
摘要

Fuzzing or fuzz testing is an established technique that aims to discover unexpected program behavior (e.g., bugs, security vulnerabilities, or crashes) by feeding automatically generated data into a program under test. However, the application of fuzzing to test Model-Driven Software Engineering (MDSE) tools is still limited because of the difficulty of existing fuzzers to provide structured, well-typed inputs, namely models that conform to typing and consistency constraints induced by a given meta-model and underlying modeling framework. By drawing from recent advances on both fuzz testing and automated model generation, we present three different approaches for fuzzing MDSE tools: A graph grammar-based fuzzer and two variants of a coverage-guided mutation-based fuzzer working with different sets of model mutation operators. Our evaluation on a set of real-world MDSE tools shows that our approaches can outperform both standard fuzzers and model generators w.r.t. their fuzzing capabilities. Moreover, we found that each of our approaches comes with its own strengths and weaknesses in terms of fault finding capabilities and the ability to cover different aspects of the system under test. Thus the approaches complement each other, forming a fuzzer suite for testing MDSE tools.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
星星之火完成签到,获得积分10
2秒前
Orange应助小武wwwww采纳,获得10
2秒前
思源应助coldfish采纳,获得10
2秒前
Leon完成签到,获得积分10
2秒前
3秒前
彭于晏应助Qionglin采纳,获得10
3秒前
香蕉觅云应助Qionglin采纳,获得10
3秒前
领导范儿应助Qionglin采纳,获得30
3秒前
东风发布了新的文献求助10
3秒前
小二郎应助Qionglin采纳,获得30
3秒前
今后应助Qionglin采纳,获得10
3秒前
在水一方应助Qionglin采纳,获得10
3秒前
bkagyin应助Qionglin采纳,获得30
4秒前
李爱国应助Qionglin采纳,获得10
4秒前
彭于晏应助Qionglin采纳,获得30
4秒前
可爱的函函应助Qionglin采纳,获得10
4秒前
5秒前
6秒前
科研通AI6.4应助菜鸟采纳,获得10
7秒前
天之饺子完成签到,获得积分10
8秒前
8秒前
芷云完成签到,获得积分10
8秒前
明理夏波完成签到,获得积分10
8秒前
8秒前
闪闪婴发布了新的文献求助10
9秒前
九斤完成签到 ,获得积分10
9秒前
10秒前
Echo完成签到,获得积分20
10秒前
sanben发布了新的文献求助10
10秒前
星辰大海应助大气以蓝采纳,获得10
11秒前
汉谟拉比完成签到,获得积分10
11秒前
11秒前
彭于晏应助独特的安波采纳,获得10
11秒前
HJJHJH发布了新的文献求助30
12秒前
12秒前
黄新雨完成签到,获得积分20
12秒前
molihuakai应助Qionglin采纳,获得10
12秒前
HB完成签到,获得积分10
13秒前
汉堡包应助lyt采纳,获得10
13秒前
NexusExplorer应助Qionglin采纳,获得10
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Reducing Compassion Fatigue, Secondary Traumatic Stress and Burnout 600
Comparative Elite Sport Development Systems, Structures and Public Policy 600
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Auslegungsgeschichte 500
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7637061
求助须知:如何正确求助?哪些是违规求助? 9210902
关于积分的说明 19757294
捐赠科研通 7204533
什么是DOI,文献DOI怎么找? 3275618
关于科研通互助平台的介绍 2437313
邀请新用户注册赠送积分活动 2272822