2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)最新文献_第9页

Focus on New Test Cases in Continuous Integration Testing based on Reinforcement Learning 基于强化学习的持续集成测试新案例研究

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI: 10.1109/QRS57517.2022.00088

Fanliang Chen, Zheng Li, Y. Shang, Yang Yang

{"title":"Focus on New Test Cases in Continuous Integration Testing based on Reinforcement Learning","authors":"Fanliang Chen, Zheng Li, Y. Shang, Yang Yang","doi":"10.1109/QRS57517.2022.00088","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00088","url":null,"abstract":"In software regression testing, newly added test cases are more likely to fail, and therefore, should be prioritized for execution. In software regression testing for continuous integration, reinforcement learning-based approaches are promising and the RETECS (Reinforced Test Case Prioritization and Selection) framework is a successful application case. RETECS uses an agent composed of a neural network to predict the priority of test cases, and the agent needs to learn from historical information to make improvements. However, the newly added test cases have no historical execution information, thus using RETECS to predict their priority is more like ‘random’. In this paper, we focus on new test cases for continuous integration testing, and on the basis of the RETECS framework, we first propose a priority assignment method for new test cases to ensure that they can be executed first. Secondly, continuous integration is a fast iterative integration method where new test cases have strong fault detection capability within the latest periods. Therefore, we further propose an additional reward method for new test cases. Finally, based on the full lifecycle management, the ‘new’ additional rewards need to be terminated within a certain period, and this paper implements an empirical study. We conducted 30 iterations of the experiment on 12 datasets and our best results were 19.24%, 10.67%, and 34.05 positions better compared to the best parameter combination in RETECS for the NAPFD (Normalized Average Percentage of Faults Detected), RECALL and TTF (Test to Fail) metrics, respectively.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116873229","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

The Use of Pretrained Model for Matching App Reviews and Bug Reports 使用预训练模型匹配应用评论和漏洞报告

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI: 10.1109/QRS57517.2022.00034

Xiaojuan Wang, Wenyu Zhang, Shanyan Lai, Chunyang Ye, Hui Zhou

{"title":"The Use of Pretrained Model for Matching App Reviews and Bug Reports","authors":"Xiaojuan Wang, Wenyu Zhang, Shanyan Lai, Chunyang Ye, Hui Zhou","doi":"10.1109/QRS57517.2022.00034","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00034","url":null,"abstract":"Matching APP reviews with bug reports can help APP developers to quickly identify new bugs from the users’ feedback. Existing solutions represent the semantics of APP reviews and bug reports via carefully designed features and models, the performance of which however depends heavily on the manually designed model and the training data set. Large-scale pretrained models can well capture the semantics of text and have demonstrated their success in many NLP tasks. Inspired by this, we explore the effect of various pretrained models on the matching accuracy of app review and bug report. We conduct a systematic study to analyze the factors of four major pretrained models (including T5, Sentence T5, Sentence MiniLM, Sentence BERT and so on) on the matching accuracy. We find that the accuracy of Sentence T5 and Sentence MiniLM in four open source applications is significantly greater than that of the state-of-the-art approach DeepMatcher. Based on the findings, we design a novel approach to match the APP reviews with bug reports based on the pretrained model Sentence T5 and Sentence MiniLM to calculate the sentence similarity. We test it on four open source applications and the results show that our method outperforms the existing solution. On average, the precision of Sentence T5 and Sentence MiniLM are increased by 17% and 13%, respectively, and the hit ratio are increased by 15% and 14%, respectively.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134104201","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

Visualization-Based Software Defect Prediction via Convolutional Neural Network with Global Self-Attention 基于全局自关注卷积神经网络的可视化软件缺陷预测

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI: 10.1109/QRS57517.2022.00029

Shaojian Qiu, Shaosheng Wang, Xuhong Tian, Mengyang Huang, Qiong Huang

引用次数: 0

Automatic Collaborative Testing of Applications Integrating Text Features and Priority Experience Replay 集成文本功能和优先级体验回放的应用程序的自动协作测试

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI: 10.1109/QRS57517.2022.00020

Lizhi Cai, Jin Wang, Mingang Chen, Jilong Wang

{"title":"Automatic Collaborative Testing of Applications Integrating Text Features and Priority Experience Replay","authors":"Lizhi Cai, Jin Wang, Mingang Chen, Jilong Wang","doi":"10.1109/QRS57517.2022.00020","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00020","url":null,"abstract":"With the popularity of deep reinforcement learning(DRL), people have great interest in using deep reinforcement learning for application automated testing. However, most automated testing methods based on reinforcement learning ignore text information, use random sampling in experience replay and ignore the characteristics of Android automated testing. To solve above problem, this paper proposes ITPRTesting(Integrated Text feature information and Priority experience in Testing). It extracts the text information in the interface and uses the BERT algorithm to generate sentence vectors. It fuses the interactive control feature diagram(ICFD), which is mentioned in the previous work, and text information as the state required by reinforcement learning. And in reinforcement learning, the priority experience replay is combined, also the traditional priority experience replay is improved. This paper has carried out experiments on 10 open source applications. The experimental results show that ITPRTesting is superior to other methods in statement coverage and branch coverage.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134312479","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

QRS 2022 Program Committee QRS 2022项目委员会

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI: 10.1109/qrs57517.2022.00008

引用次数: 0

API Misuse Detection Method Based on Transformer 基于变压器的API误用检测方法

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI: 10.1109/QRS57517.2022.00100

Jingbo Yang, Jian Ren, Wenjun Wu

{"title":"API Misuse Detection Method Based on Transformer","authors":"Jingbo Yang, Jian Ren, Wenjun Wu","doi":"10.1109/QRS57517.2022.00100","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00100","url":null,"abstract":"Software developers need to take advantage of a variety of APIs (application programming interface) in their programs to implement specific functions. The problem of API misuses often arises when developers have incorrect understandings about the new APIs without carefully reading API documents. In order to avoid software defects caused by API misuse, researchers have explored multiple methods, including using AI(artificial intelligence) technology.As a kind of neural network in AI, Transformer has a good sequence processing ability, and the self attention mechanism used by Transformer can better catch the relation in a sequence or between different sequences. Besides it has a good model interpretability. From the perspective of combining API misuse detection with AI, this paper implements a standard Transformer model and a target-combination Transformer model to the learning of API usage information in a named API call sequence extracted from API usage program code. Then we present in the paper the way that our models use API usage information to detect if an API is misused in code. We use F1, precision and recall to evaluate the detection ability and show the advantages of our models in these three indexes. Besides, our models based on Transformer both have a better convergence. Finally, this paper explains why the models based on Transformer has a better performance by showing attention weight among different elements in code.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115967574","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

CRUST: Towards a Unified Cross-Language Program Analysis Framework for Rust 面向Rust的统一跨语言程序分析框架

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI: 10.1109/QRS57517.2022.00101

Shuang Hu, Baojian Hua, Lei Xia, Yang Wang

{"title":"CRUST: Towards a Unified Cross-Language Program Analysis Framework for Rust","authors":"Shuang Hu, Baojian Hua, Lei Xia, Yang Wang","doi":"10.1109/QRS57517.2022.00101","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00101","url":null,"abstract":"Rust is a new safe system programming language enforcing safety guarantees by novel language features, a rich type system, and strict compile-time checking rules, and thus has been used extensively to build system software. For multilingual Rust applications containing external C code, memory security vulnerabilities can occur due to the intrinsically unsafe nature of C and the improper interactions between Rust and C. Unfortunately, existing security studies on Rust only focus on pure Rust code but cannot analyze either the native C code or the Rust/C interactions in multilingual Rust applications. As a result, the lack of such studies may defeat the guarantee that Rust is a safe language.This paper presents CRust, a unified program analysis framework across Rust and C, which enables program analyses to understand the semantics of C code by translating Rust and C into a unified specification language. The CRust framework consists of three key components: (1) a unified specification language CRustIR, which is a strong-typed low-level intermediate language suitable for program analysis; (2) a transformation to build models of C code by converting C code into CRustIR; and (3) program analysis algorithms on CRustIR to detect security vulnerabilities. We have implemented a software prototype for CRust, and have conducted extensive experiments to evaluate its effectiveness and performance. Experimental results demonstrated that CRust can effectively detect common memory security vulnerabilities caused by the interaction of Rust and C that are missed by state-of-the-art tools. In addition, CRust is efficient in bringing negligible overhead (0.23 seconds on average).","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116189126","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

QRS 2022 Organizing Committee QRS 2022组委会

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI: 10.1109/qrs57517.2022.00007

引用次数: 0

Evaluation of the Architecture Alternatives for Real-Time Intrusion Detection Systems for Vehicles 车辆实时入侵检测系统体系结构替代方案的评估

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI: 10.1109/QRS57517.2022.00091

Mubark Jedh, Jian Kai Lee, L. B. Othmane

引用次数: 0

Scriptless GUI Testing on Mobile Applications 移动应用程序的无脚本GUI测试

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI: 10.1109/QRS57517.2022.00113

Thorn Jansen, Fernando Pastor Ricós, Yaping Luo, K. Vlist, R. V. Dalen, Pekka Aho, T. Vos

{"title":"Scriptless GUI Testing on Mobile Applications","authors":"Thorn Jansen, Fernando Pastor Ricós, Yaping Luo, K. Vlist, R. V. Dalen, Pekka Aho, T. Vos","doi":"10.1109/QRS57517.2022.00113","DOIUrl":"https://doi.org/10.1109/QRS57517.2022.00113","url":null,"abstract":"Traditionally, end-to-end testing of mobile apps is either performed manually or automated with test scripts. However, manual GUI testing is expensive and slow, and test scripts are fragile for GUI changes, resulting in high maintenance costs. Scriptless testing attempts to address the costs associated with GUI testing. Existing scriptless approaches for mobile testing do not seem to fit the requirements of the industry, specifically those of the ING. This study presents an extension to open source TESTAR tool to support scriptless GUI testing of Android and iOS applications. We present an initial validation of the tool on an industrial setting at the ING. From the validation, we determine that the extended TESTAR outperforms two other state-of-the-art scriptless testing tools for Android in terms of code coverage, and achieves similar performance as the scripted test automation already in use at the ING. Moreover, we see that the scriptless approach covers parts of the application under test that the existing test scripts did not cover, showing the complementarity of the approaches, providing more value for the testers.","PeriodicalId":143812,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122360722","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