2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)最新文献_第7页

Efficient Incrementalized Runtime Checking of Linear Measures on Lists 列表上线性度量的高效增量运行时检查

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST) Pub Date : 2017-03-01 DOI: 10.1109/ICST.2017.35

A. Gyori, P. Garg, E. Pek, P. Madhusudan

{"title":"Efficient Incrementalized Runtime Checking of Linear Measures on Lists","authors":"A. Gyori, P. Garg, E. Pek, P. Madhusudan","doi":"10.1109/ICST.2017.35","DOIUrl":"https://doi.org/10.1109/ICST.2017.35","url":null,"abstract":"We present mechanisms to specify and efficiently check, at runtime, assertions that express structural properties and aggregate measures of dynamically manipulated linkedlist data structures. Checking assertions involving the structure, disjointness, and aggregation measures on lists and list segments typically requires linear or quadratic time in the size of the heap. Our main contribution is an incrementalization instrumentation that tracks properties of data structures dynamically as the program executes and leads to orders of magnitude speedup in assertion checking in many scenarios. Our incrementalization incurs a constant overhead on updates to list structures but enables checking assertions in constant time, independent of the size of the heap. We define a general class of functions on lists, called linear measures, which are amenable to our incrementalization technique. We demonstrate the effectiveness of our technique by showing orders of magnitude speedup in two scenarios: one scenario stemming from assertions at the level of APIs of list-manipulating libraries and the other scenario stemming from providing dynamic detection of security attacks caused by malicious rootkits.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130284333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Framework for Failure Diagnosis 故障诊断框架

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST) Pub Date : 2017-03-01 DOI: 10.1109/ICST.2017.75

Mojdeh Golagha

引用次数: 0

SAGA Toolbox: Interactive Testing of Guarded Assertions SAGA工具箱:保护断言的交互式测试

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST) Pub Date : 2017-03-01 DOI: 10.1109/ICST.2017.59

Daniel Flemström, T. Gustafsson, A. Kobetski

引用次数: 3

Generic and Effective Specification of Structural Test Objectives 结构测试目标的通用和有效规范

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST) Pub Date : 2016-09-05 DOI: 10.1109/ICST.2017.48

M. Marcozzi, Mickaël Delahaye, Sébastien Bardin, N. Kosmatov, V. Prevosto

引用次数: 15

Uncertainty-Driven Black-Box Test Data Generation 不确定性驱动的黑盒测试数据生成

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST) Pub Date : 2016-08-10 DOI: 10.1109/ICST.2017.30

Neil Walkinshaw, G. Fraser

{"title":"Uncertainty-Driven Black-Box Test Data Generation","authors":"Neil Walkinshaw, G. Fraser","doi":"10.1109/ICST.2017.30","DOIUrl":"https://doi.org/10.1109/ICST.2017.30","url":null,"abstract":"We can never be certain that a software system is correct simply by testing it, but with every additional successful test we become less uncertain about its correctness. In absence of source code or elaborate specifications and models, tests are usually generated or chosen randomly. However, rather than randomly choosing tests, it would be preferable to choose those tests that decrease our uncertainty about correctness the most. In order to guide test generation, we apply what is referred to in Machine Learning as \"Query Strategy Framework\": We infer a behavioural model of the system under test and select those tests which the inferred model is \"least certain\" about. Running these tests on the system under test thus directly targets those parts about which tests so far have failed to inform the model. We provide an implementation that uses a genetic programming engine for model inference in order to enable an uncertainty sampling technique known as \"query by committee\", and evaluate it on eight subject systems from the Apache Commons Math framework and JodaTime. The results indicate that test generation using uncertainty sampling outperforms conventional and Adaptive Random Testing.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126395080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

Assessing and Improving the Mutation Testing Practice of PIT PIT突变检测实践的评估与改进

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST) Pub Date : 2016-01-11 DOI: 10.1109/ICST.2017.47

Thomas Laurent, Anthony Ventresque, Mike Papadakis, Christopher Henard, Yves Le Traon

{"title":"Assessing and Improving the Mutation Testing Practice of PIT","authors":"Thomas Laurent, Anthony Ventresque, Mike Papadakis, Christopher Henard, Yves Le Traon","doi":"10.1109/ICST.2017.47","DOIUrl":"https://doi.org/10.1109/ICST.2017.47","url":null,"abstract":"Mutation testing is extensively used in software testing studies. However, popular mutation testing tools use a restrictive set of mutants which does not conform to the community standards and mutation testing literature. This can be problematic since the effectiveness of mutation strongly depends on the used mutants. To investigate this issue we form an extended set of mutants and implement it on a popular mutation testing tool named PIT. We then show that in real-world projects the original mutants of PIT are easier to kill and lead to tests that score statistically lower than those of the extended set of mutants for a range of 35% to 70% of the studied classes. These results raise serious concerns regarding the validity of mutation-based experiments that use PIT. To further show the strengths of the extended mutants we also performed an analysis using a benchmark with mutation-adequate test cases and identified equivalent mutants. Our results confirmed that the extended mutants are more effective than a) the original version of PIT and b) two other popular mutation testing tools (major and muJava). In particular, our results demonstrate that the extended mutants are more effective by 23%, 12% and 7% than the mutants of the original PIT, major and muJava. They also show that the extended mutants are at least as strong as the mutants of all the other three tools together. To support future research, we make the new version of PIT, which is equipped with the extended mutants, publicly available.","PeriodicalId":112258,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125659390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 53

The Fitness Function for the Job: Search-Based Generation of Test Suites That Detect Real Faults 任务的适应度函数:基于搜索的检测真实故障的测试套件生成

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST) Pub Date : 1900-01-01 DOI: 10.1109/ICST.2017.38

Gregory Gay

引用次数: 32