2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)最新文献

Supervised Learning for Test Suit Selection in Continuous Integration 持续集成中测试服选择的监督学习

2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2021-04-01 DOI: 10.1109/ICSTW52544.2021.00048

Ricardo Martins, R. Abreu, Manuel Lopes, J. Nadkarni

引用次数: 4

Combinatorial Testing Metrics for Machine Learning 机器学习的组合测试指标

2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2021-04-01 DOI: 10.1109/ICSTW52544.2021.00025

Erin Lanus, Laura J. Freeman, D. R. Kuhn, R. Kacker

引用次数: 15

Prioritized Test Generation Guided by Software Fault Prediction 基于软件故障预测的优先级测试生成

2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2021-04-01 DOI: 10.1109/ICSTW52544.2021.00045

Eran Hershkovich, Roni Stern, R. Abreu, Amir Elmishali

{"title":"Prioritized Test Generation Guided by Software Fault Prediction","authors":"Eran Hershkovich, Roni Stern, R. Abreu, Amir Elmishali","doi":"10.1109/ICSTW52544.2021.00045","DOIUrl":"https://doi.org/10.1109/ICSTW52544.2021.00045","url":null,"abstract":"Writing and running software unit tests is one of the fundamental techniques used to maintain software quality. However, this process is rather costly and time consuming. Thus, much effort has been devoted to generating unit tests automatically. The common objective of test generation algorithms is to maximize code coverage. However, maximizing coverage is not necessarily correlated with identifying faults [1]. In this work, we propose a novel approach for test generation aiming at generating a small set of tests that cover the software components that are likely to contain bugs. To identify which components are more likely to contain bugs, we train a software fault prediction model using machine learning techniques. We implemented this approach in practice in a tool called QUADRANT, and demonstrate its effectiveness on five real-world, open-source projects. Results show the benefit of using QUADRANT, where test generation guided by our fault prediction model can detect more than double the number of bugs compared to a coverage-oriented approach, thereby saving test generation and execution efforts.","PeriodicalId":371680,"journal":{"name":"2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126934761","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: 3

Process Mining on a Robotic Mechanism 基于机器人机构的过程挖掘

2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2021-04-01 DOI: 10.1109/ICSTW52544.2021.00043

Turcanu Cristina Nicoleta

{"title":"Process Mining on a Robotic Mechanism","authors":"Turcanu Cristina Nicoleta","doi":"10.1109/ICSTW52544.2021.00043","DOIUrl":"https://doi.org/10.1109/ICSTW52544.2021.00043","url":null,"abstract":"As the Fourth Industrial revolution is an emerging trend articulated by the rapid advances in automation technologies, the use of robots and the Industrial Internet of Things (IIoT) is meant to significantly simplify human’s life. However, if we consider industrial robots with two or more axes that could have unpredictable motions and velocities, real-time robots’ reliability is a matter that is strongly related to safety requirements. Considering operational testing in the context of IIoT, process conformance verification could be an important asset for obtaining knowledge of the failures – in particular, discovering failure occurrence patterns and establishing realtime preventive actions.This paper aims to pave a way to implementing a methodology for a robotic mechanism process conformance checking, using some certain synthetic operational traces that could be collected through IIoT technologies and communicated through computer driven industrial applications. Moreover, the paper suggests a formal verification method against the standard process model that could be provided by the robot manufacturer. Besides presenting some process conformance verification aspects using Celonis process mining tool, this study also refers to some finite state machine (FSM) and business process modelling (BPMN) representation, suggested as being suitable for robots’ formal specifications.","PeriodicalId":371680,"journal":{"name":"2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127950151","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

Computing Sequence Covering Arrays using Unified Combinatorial Interaction Testing 基于统一组合交互测试的计算序列覆盖阵列

2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2021-04-01 DOI: 10.1109/ICSTW52544.2021.00024

Hanefi Mercan, Cemal Yilmaz

引用次数: 4

SYSMODIS: A Systematic Model Discovery Approach 系统的模型发现方法

2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2021-04-01 DOI: 10.1109/ICSTW52544.2021.00023

Omer Korkmaz, Cemal Yilmaz

{"title":"SYSMODIS: A Systematic Model Discovery Approach","authors":"Omer Korkmaz, Cemal Yilmaz","doi":"10.1109/ICSTW52544.2021.00023","DOIUrl":"https://doi.org/10.1109/ICSTW52544.2021.00023","url":null,"abstract":"In this paper, we present an automated model discovery approach, called SYSMODIS, which uses covering arrays to systematically sample the input spaces. SYSMODIS discovers finite state machine-based models, where states represent distinct screens and the edges between the states represent the transitions between the screens. SYSMODIS also discovers the likely guard conditions for the transitions, i.e., the conditions that must be satisfied before the transitions can be taken. For the first time a previously unseen screen is visited, a covering array-based test suite for the input fields present on the screen as well as the actions that can be taken on the screen, is created. SYSMODIS keeps on crawling until all the test suites for all the screens have been exhaustively tested. Once the crawling is over, the results of the test suites are fed to a machine learning algorithm on a per screen basis to determine the likely guard conditions. In the experiments we carried out to evaluate the proposed approach, we observed that SYSMODIS profoundly improved the state/screen coverage, transition coverage, and/or the accuracy of the predicted guard conditions, compared to the existing approaches studied in the paper.","PeriodicalId":371680,"journal":{"name":"2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128344823","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: 3

Using Advanced Code Analysis for Boosting Unit Test Creation 使用高级代码分析促进单元测试创建

2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2021-04-01 DOI: 10.1109/ICSTW52544.2021.00053

M. Zielinski, R. Groenboom

引用次数: 3

AutoKG - An Automotive Domain Knowledge Graph for Software Testing: A position paper AutoKG -用于软件测试的汽车领域知识图谱:意见书

2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2021-04-01 DOI: 10.1109/ICSTW52544.2021.00047

Vaibhav Kesri, Anmol Nayak, Karthikeyan Ponnalagu

{"title":"AutoKG - An Automotive Domain Knowledge Graph for Software Testing: A position paper","authors":"Vaibhav Kesri, Anmol Nayak, Karthikeyan Ponnalagu","doi":"10.1109/ICSTW52544.2021.00047","DOIUrl":"https://doi.org/10.1109/ICSTW52544.2021.00047","url":null,"abstract":"Industries have a significant amount of data in semi-structured and unstructured formats which are typically captured in text documents, spreadsheets, images, etc. This is especially the case with the software description documents used by domain experts in the automotive domain to perform tasks at various phases of the Software Development Life Cycle (SDLC). In this paper, we propose an end-to-end pipeline to extract an Automotive Knowledge Graph (AutoKG) from textual data using Natural Language Processing (NLP) techniques with the application of automatic test case generation. The proposed pipeline primarily consists of the following components: 1) AutoOntology, an ontology that has been derived by analyzing several industry scale automotive domain software systems, 2) AutoRE, a Relation Extraction (RE) model to extract triplets from various sentence types typically found in the automotive domain, and 3) AutoVec, a neural embedding based algorithm for triplet matching and context-based search. We demonstrate the pipeline with an application of automatic test case generation from requirements using AutoKG.","PeriodicalId":371680,"journal":{"name":"2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129012231","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: 4

DeepRace: A learning-based data race detector DeepRace:一个基于学习的数据竞赛检测器

2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2021-04-01 DOI: 10.1109/ICSTW52544.2021.00046

Ali Tehrani Jamsaz, Mohammed Khaleel, R. Akbari, A. Jannesari

引用次数: 4

2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) Pub Date : 2021-04-01 DOI: 10.1109/ICSTW52544.2021.00037

Satoshi Masuda, T. Matsuodani, K. Tsuda

{"title":"Syntax-Tree Similarity for Test-Case Derivability in Software Requirements","authors":"Satoshi Masuda, T. Matsuodani, K. Tsuda","doi":"10.1109/ICSTW52544.2021.00037","DOIUrl":"https://doi.org/10.1109/ICSTW52544.2021.00037","url":null,"abstract":"Software testing has been important for software engineering to contribute to developing high-quality software. Decision table testing is a general technique to derive test cases with information on conditions and actions from software requirements. Deriving conditions and actions from requirements is key for efficient decision table testing. This paper proposes and evaluates a syntax-tree similarity method for test-case derivability in software requirements. We define the syntax-tree similarity technique used in our method as selecting test-case-derivable sentences from requirements at pre-processing. The syntax tree is defined as divided into sub-trees that consist of a root to each leaf. The syntax-tree similarity technique calculates the similarity between each sentence in the requirements and test-case-derivable sentence. The method involves natural language processing to select test-case-derivable sentences from the requirements on the basis of syntax-tree similarity then determines conditions and actions through dependency and case analyses. After selecting requirements by syntax-tree similarity, our method derives conditions and actions from the requirements by the deriving rules we define. Experiments revealed that the F-measure of the accuracy of the derived conditions and actions increased 16% from that reported in prior work. The results from case studies further indicate the effectiveness of our method.","PeriodicalId":371680,"journal":{"name":"2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115208678","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: 0