2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)最新文献_第5页

ICST 2020 Opinion

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI: 10.1109/icst46399.2020.00005

引用次数: 0

Source-Codeless Testing for Android Apps Android应用的无源代码测试

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI: 10.1109/icst46399.2020.00057

Camilo Escobar-Velásquez

引用次数: 0

Substate Profiling for Enhanced Fault Detection and Localization: An Empirical Study 基于亚状态分析的故障检测与定位方法研究

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI: 10.1109/ICST46399.2020.00013

R. A. Assi, Wes Masri, Chadi Trad

{"title":"Substate Profiling for Enhanced Fault Detection and Localization: An Empirical Study","authors":"R. A. Assi, Wes Masri, Chadi Trad","doi":"10.1109/ICST46399.2020.00013","DOIUrl":"https://doi.org/10.1109/ICST46399.2020.00013","url":null,"abstract":"Researchers have used execution profiles to enable coverage-based techniques in areas such as defect detection and fault localization. Typical profile elements include functions, statements, and branches, which are structural in nature. Such elements might not always discriminate failing runs from passing runs, which renders them ineffective in some cases. This motivated us to investigate alternative profiles, namely, substate profiles that aim at approximating the state of a program (as opposed to its execution path). Substate profiling is a recently presented form of state profiling that is practical, fine-grained, and generic enough to be applicable to various profile-based analyses.This paper presents an empirical study demonstrating how complementing structural profiles with substate profiles would benefit Test Suite Reduction (TSR), Test Case Prioritization (TCP), and Spectrum-based Fault Localization (SBFL). Using the Defects4J benchmark, we contrasted the effectiveness of TSR, TCP, and SBFL when using the structural profiles only to when using the concatenation of the structural and substate profiles. Leveraging substate profiling enhanced the effectiveness of all three techniques. For example: 1) For TSR, 86 more versions exhibited 100% defect detection rate. 2) For TCP, 22 more versions had one of their failing tests ranked among the top 20%. 3) For SBFL,substate profiling localized 14 faults that structural profiling failed to localize. Furthermore, our study showed that the improvement due tosubstate profiling was noticeably more significant in the presence of coincidentally correct tests than in their absence. This positions substate profiling as a promising basis for mitigating the negative effect of coincidental correctness.","PeriodicalId":235967,"journal":{"name":"2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)","volume":"2004 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125789858","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

Scaling Test Case Generation For Expressive Decision Tables 表达性决策表的扩展测试用例生成

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI: 10.1109/icst46399.2020.00044

Supriya Agrawal, R. Venkatesh, U. Shrotri, Amey Zare, S. Verma

{"title":"Scaling Test Case Generation For Expressive Decision Tables","authors":"Supriya Agrawal, R. Venkatesh, U. Shrotri, Amey Zare, S. Verma","doi":"10.1109/icst46399.2020.00044","DOIUrl":"https://doi.org/10.1109/icst46399.2020.00044","url":null,"abstract":"Conventional automated test case generation techniques do not scale to modern software systems, as these systems have a large number of requirements that change frequently. In this paper, we present a scalable algorithm, AGenT, that generates test cases to cover maximal requirements.AGenT takes Expressive Decision Tables (EDT), specifying requirements of a system, as input and realises these as multiple Discrete Time Automata (DTAs). AGenT then generates test cases to cover each row of the tables. To improve scalability, it attempts to cover nearer rows (requiring fewer inputs) first, where distance is measured using a novel distance-to-match heuristic. It also maintains information about desirability and predictability of inputs so as to select promising inputs with a higher probability. Although the algorithm has been presented in the context of EDT, it operates on its DTA representation and hence can be applied to any system that is represented as a collection of DTAs like Statemate and Stateflow. In this paper, we describe AGenT in detail and present findings from two experiments that we conducted. We compared AGenT with state-of-the-art algorithms, DRAFT and a random test case generation algorithm, RTG. In the first experiment, AGenT took a maximum of 144 seconds to cover all rows whereas the other two algorithms timed out on many modules. In the second experiment, for a module with 701 rows, AGenT achieved 7% more coverage than DRAFT and 12% more than RTG.","PeriodicalId":235967,"journal":{"name":"2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126827290","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: 2

Tool Support for Refactoring Manual Tests 对重构手工测试的工具支持

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI: 10.1109/icst46399.2020.00041

Élodie Bernard, Julien Botella, F. Ambert, B. Legeard, M. Utting

引用次数: 2

Parallelization of Classical Numerical optimization in Quantum Variational Algorithms 量子变分算法中经典数值优化的并行化

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI: 10.1109/ICST46399.2020.00039

Marco Pistoia, Peng Liu, Chun Chen, Shaohan Hu, Stephen P Wood

{"title":"Parallelization of Classical Numerical optimization in Quantum Variational Algorithms","authors":"Marco Pistoia, Peng Liu, Chun Chen, Shaohan Hu, Stephen P Wood","doi":"10.1109/ICST46399.2020.00039","DOIUrl":"https://doi.org/10.1109/ICST46399.2020.00039","url":null,"abstract":"Numerical optimization has been extensively used in many real-world applications related to Scientific Computing, Artificial Intelligence and, more recently, Quantum Computing. However, existing optimizers conduct their internal computations sequentially, which affects their performance. We observed a general pattern that enabled us to parallelize such internal computations and achieve significant speedup. We designed a novel parallelization algorithm for optimizers, which consists of pattern detection, prediction, precomputation, and caching. Importantly, our design does not require any change to the optimizers. Instead, it simply modifies the function to be optimized, thereby leading to several engineering advantages, including simplicity, modularity and portability. We implemented this solution and included it in the Qiskit Aqua open-source project. In this paper, we present an evaluation on both standard benchmarks and real-world quantum-computing applications. The evaluation results confirm that our approach (1) incurs negligible overhead, (2) effectively speeds up optimization, and (3) does not affect the accuracy of the results or the convergence of the optimizers.","PeriodicalId":235967,"journal":{"name":"2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130900878","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

Optimizing Mutation Testing by Discovering Dynamic Mutant Subsumption Relations 通过发现动态突变包容关系优化突变检测

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI: 10.1109/icst46399.2020.00029

M. Guimarães, Leo Fernandes, Márcio Ribeiro, Marcelo d’Amorim, Rohit Gheyi

{"title":"Optimizing Mutation Testing by Discovering Dynamic Mutant Subsumption Relations","authors":"M. Guimarães, Leo Fernandes, Márcio Ribeiro, Marcelo d’Amorim, Rohit Gheyi","doi":"10.1109/icst46399.2020.00029","DOIUrl":"https://doi.org/10.1109/icst46399.2020.00029","url":null,"abstract":"One recent promising direction on reducing costs of mutation analysis is to identify redundant mutations, i.e., mutations that are subsumed by some other mutations. Previous works found out redundant mutants manually through the truth table. Although the idea is promising, it can only be applied for logical and relational operators. In this paper, we propose an approach to discover redundancy in mutations through dynamic subsumption relations among mutants. We focus on subsumption relations among mutations of an expression or statement, named here as “mutation target:” By focusing on targets and relying on automatic test generation tools, we define subsumption relations for dozens of mutation targets in which the MUJAVA tool can apply mutations. We then implemented these relations in a tool, named MUJAVA-M, that generates a reduced set of mutants for each target, avoiding redundant mutants. We evaluated MUJAVA and MUJAVA-M using classes of five open-source projects. As results, we analyze 2,341 occurrences of 32 mutation targets in 168 classes. MUJAVA-M generates less mutants (on average 64.43% less) with 100% of effectiveness in 20 out of 32 targets and more than 95% in 29 out of 32 mutation targets. MUJAVA- M also reduced the time to execute the test suites against the mutants in 52.53% on average, considering the full mutation analysis process.","PeriodicalId":235967,"journal":{"name":"2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133889537","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: 15

ICST 2020 Index

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI: 10.1109/icst46399.2020.00064

引用次数: 0

One-click formal methods 一键式形式化方法

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI: 10.1109/icst46399.2020.00009

Liana Hadarean

引用次数: 0

ICST 2020 Committees ICST 2020委员会

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2020-10-01 DOI: 10.1109/icst46399.2020.00007

引用次数: 0