2022 IEEE 29th Annual Software Technology Conference (STC)最新文献_第3页

Bayesian Approach for Regression Testing (BART) using Test Suite Prioritization 使用测试套件优先级的回归测试贝叶斯方法(BART)

2022 IEEE 29th Annual Software Technology Conference (STC) Pub Date : 2022-10-01 DOI: 10.1109/STC55697.2022.00027

Prabuddh Gupta, Divya Balakrishna, Rohit R. Shende, Vikram Raina, Shalini Lal, Aditya Doshatti, Lalitha Sripada, Mitesh Sharma, Shiva Thamilavel

{"title":"Bayesian Approach for Regression Testing (BART) using Test Suite Prioritization","authors":"Prabuddh Gupta, Divya Balakrishna, Rohit R. Shende, Vikram Raina, Shalini Lal, Aditya Doshatti, Lalitha Sripada, Mitesh Sharma, Shiva Thamilavel","doi":"10.1109/STC55697.2022.00027","DOIUrl":"https://doi.org/10.1109/STC55697.2022.00027","url":null,"abstract":"Majority of the current state-of-the-art test suite (TS) prioritization algorithms for black-box testing focus on improving average percentage of fault detected (APFD) metric. These however suffer from two critical challenges 1) high time complexity of $ge O(n^{2})$ where n is the number of test suites, and 2) limited ability to self-stop TS Prioritization (TSP) computation if the system under test (SUT) becomes highly stable. In this work we present an approach to overcome these two challenges while achieving high APFD efficiency over the conventional random ordering. A novel algorithm called Bayesian approach to regression testing (BART) is developed herein which models continuous integration (CI) cycle’s attributes like test suite life cycle (TSLC), stability and bugs as Bayesian inference pattern namely Dirichlet-Multinomial model. This work demonstrates that BART’s APFD metrics improve significantly in comparison to conventional random ordering and therefore this approach achieves for the first time a complexity of $O(nlogn)$ for black-box based test prioritization.","PeriodicalId":170123,"journal":{"name":"2022 IEEE 29th Annual Software Technology Conference (STC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116253643","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

A Distributed Ledger Technology Design using Hyperledger Fabric and a Clinical Trial Use Case 使用超级账本结构的分布式账本技术设计和临床试验用例

2022 IEEE 29th Annual Software Technology Conference (STC) Pub Date : 2022-10-01 DOI: 10.1109/STC55697.2022.00031

R. Kuhn, Joshua D. Roberts, David F. Ferraiolo, J. Defranco

引用次数: 0

ExClaim: Explainable Neural Claim Verification Using Rationalization ExClaim:使用合理化的可解释神经索赔验证

2022 IEEE 29th Annual Software Technology Conference (STC) Pub Date : 2022-10-01 DOI: 10.1109/STC55697.2022.00012

Sai Gurrapu, Lifu Huang, Feras A. Batarseh

{"title":"ExClaim: Explainable Neural Claim Verification Using Rationalization","authors":"Sai Gurrapu, Lifu Huang, Feras A. Batarseh","doi":"10.1109/STC55697.2022.00012","DOIUrl":"https://doi.org/10.1109/STC55697.2022.00012","url":null,"abstract":"With the advent of deep learning, text generation language models have improved dramatically, with text at a similar level as human-written text. This can lead to rampant misinformation because content can now be created cheaply and distributed quickly. Automated claim verification methods exist to validate claims, but they lack foundational data and often use mainstream news as evidence sources that are strongly biased towards a specific agenda. Current claim verification methods use deep neural network models and complex algorithms for a high classification accuracy but it is at the expense of model explainability. The models are black-boxes and their decision-making process and the steps it took to arrive at a final prediction are obfuscated from the user. We introduce a novel claim verification approach, namely: ExClaim, that attempts to provide an explainable claim verification system with foundational evidence. Inspired by the legal system, ExClaim leverages rationalization to provide a verdict for the claim and justifies the verdict through a natural language explanation (rationale) to describe the model’s decision-making process. ExClaim treats the verdict classification task as a question-answer problem and achieves a performance of 0.93 F1 score. It provides subtasks explanations to also justify the intermediate outcomes. Statistical and Explainable AI (XAI) evaluations are conducted to ensure valid and trustworthy outcomes. Ensuring claim verification systems are assured, rational, and explainable is an essential step toward improving Human-AI trust and the accessibility of black-box systems.","PeriodicalId":170123,"journal":{"name":"2022 IEEE 29th Annual Software Technology Conference (STC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133089647","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

AI Assurance for the Public – Trust but Verify, Continuously 公众的人工智能保证——信任，但要不断验证

2022 IEEE 29th Annual Software Technology Conference (STC) Pub Date : 2022-10-01 DOI: 10.1109/STC55697.2022.00032

P. Laplante, Rick Kuhn

引用次数: 0

A Modular and Expandable Testbed for Evaluating ML-based Bug Finders 基于ml的Bug查找器的模块化和可扩展测试平台

2022 IEEE 29th Annual Software Technology Conference (STC) Pub Date : 2022-10-01 DOI: 10.1109/STC55697.2022.00017

Philippe Dessauw, A. Delaitre, Hialo Muniz Carvalho, Vadim Okun

引用次数: 1

Proxy Verification and Validation For Critical Autonomous and AI Systems 关键自治和人工智能系统的代理验证和验证

2022 IEEE 29th Annual Software Technology Conference (STC) Pub Date : 2022-10-01 DOI: 10.1109/STC55697.2022.00014

P. Laplante, M. Kassab, J. Defranco

{"title":"Proxy Verification and Validation For Critical Autonomous and AI Systems","authors":"P. Laplante, M. Kassab, J. Defranco","doi":"10.1109/STC55697.2022.00014","DOIUrl":"https://doi.org/10.1109/STC55697.2022.00014","url":null,"abstract":"A challenging problem for software and systems engineers is to provide assurance of operations for a system that is critical but must operate in situations that cannot be easily created in the testing lab. For example, a space system cannot be fully tested in all operational modes until it is launched and nuclear power plants cannot be tested under real critical temperature overload conditions. This situation is particularly challenging when seeking to provide assurance in critical AI systems (CAIS) where the underlying algorithms may be very difficult to verify under any conditions. In these cases using systems that have a similar underlying application, operational profiles, user characteristics, and underlying AI algorithms may be suitable as testing proxies. For example, a robot vacuum may have significant operational and implementation similarities to act as a testing proxy for some aspects of an autonomous vehicle.In this work we discuss the challenges in assured autonomy for CAIS and suggest a way forward using proxy systems. We describe a methodology for characterizing CAIS and matching them to their non-critical proxy equivalent. Examples are given along with a discussion of the history of other kinds of proxy verification and validation","PeriodicalId":170123,"journal":{"name":"2022 IEEE 29th Annual Software Technology Conference (STC)","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123365773","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

A Layered Reference Model for Penetration Testing with Reinforcement Learning and Attack Graphs 基于强化学习和攻击图的渗透测试分层参考模型

2022 IEEE 29th Annual Software Technology Conference (STC) Pub Date : 2022-06-14 DOI: 10.1109/STC55697.2022.00015

Tyler Cody

{"title":"A Layered Reference Model for Penetration Testing with Reinforcement Learning and Attack Graphs","authors":"Tyler Cody","doi":"10.1109/STC55697.2022.00015","DOIUrl":"https://doi.org/10.1109/STC55697.2022.00015","url":null,"abstract":"This paper considers key challenges to using re-inforcement learning (RL) with attack graphs to automate penetration testing in real-world applications from a systems perspective. RL approaches to automated penetration testing are actively being developed, but there is no consensus view on the representation of computer networks with which RL should be interacting. Moreover, there are significant open challenges to how those representations can be grounded to the real networks where RL solution methods are applied. This paper elaborates on representation and grounding using topic challenges of interacting with real networks in real-time, emulating realistic adversary behavior, and handling unstable, evolving networks. These challenges are both practical and mathematical, and they directly concern the reliability and dependability of penetration testing systems. This paper proposes a layered reference model to help organize related research and engineering efforts. The presented layered reference model contrasts traditional models of attack graph workflows because it is not scoped to a sequential, feed-forward generation and analysis process, but to broader aspects of lifecycle and continuous deployment. Researchers and practitioners can use the presented layered reference model as a first-principles outline to help orient the systems engineering of their penetration testing systems.","PeriodicalId":170123,"journal":{"name":"2022 IEEE 29th Annual Software Technology Conference (STC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115644886","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: 7

Fault Localization in Cloud using Centrality Measures 基于中心性度量的云故障定位

2022 IEEE 29th Annual Software Technology Conference (STC) Pub Date : 2021-09-23 DOI: 10.1109/STC55697.2022.00033

R. NarayanaaS, M. Sivaranjan, S. LekshmiR

引用次数: 0