Pub Date : 2024-06-01DOI: 10.1109/mrl.2024.3389635
Justin Ray
Checksums play a vital role in detecting data corruption in digital storage and communication systems. The use of some kind of checksum is almost ubiquitous unless more complex error-correcting codes are used. Nevertheless, the details of these checksums—how and why they work, and what is to be gained by choosing one over another—are often overlooked. Philip Koopman’s book, Understanding Checksums and Cyclic Redundancy Checks, provides an approachable, practical, and thorough guide on this topic that should be on the shelf of anyone tasked with selecting or implementing these checksums, especially for high-reliability or safety-critical applications.
在数字存储和通信系统中，校验和在检测数据损坏方面起着至关重要的作用。除非使用更复杂的纠错码，否则某种校验和的使用几乎无处不在。然而，这些校验和的细节--它们如何工作、为什么工作，以及选择一种校验和比选择另一种校验和有什么好处--往往被忽视。菲利普-库普曼（Philip Koopman）的《理解校验和与循环冗余校验》（Understanding Checksums and Cyclic Redundancy Checks）一书就这一主题提供了一本平易近人、实用而全面的指南，任何负责选择或实施这些校验和的人都应将其放在书架上，尤其是在高可靠性或安全关键型应用中。
{"title":"Review of Understanding Checksums and Cyclic Redundancy Checks—Philip Koopman (Boca Raton, FL, USA: CRC Press, 2024)","authors":"Justin Ray","doi":"10.1109/mrl.2024.3389635","DOIUrl":"https://doi.org/10.1109/mrl.2024.3389635","url":null,"abstract":"Checksums play a vital role in detecting data corruption in digital storage and communication systems. The use of some kind of checksum is almost ubiquitous unless more complex error-correcting codes are used. Nevertheless, the details of these checksums—how and why they work, and what is to be gained by choosing one over another—are often overlooked. Philip Koopman’s book, Understanding Checksums and Cyclic Redundancy Checks, provides an approachable, practical, and thorough guide on this topic that should be on the shelf of anyone tasked with selecting or implementing these checksums, especially for high-reliability or safety-critical applications.","PeriodicalId":517825,"journal":{"name":"IEEE Reliability Magazine","volume":"8 13","pages":"46-47"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141274338","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}
Pub Date : 2024-06-01DOI: 10.1109/mrl.2024.3385733
Louis J. Gullo
The Reliability Society’s Standards Committee (IEEE RS-SC) is an IEEE group developing and sustaining standards for the reliability engineering profession. The IEEE Reliability Society (RS) develops open consensus-based reliability engineering and related engineering standards using the IEEE Standards Association (SA standards development process). The IEEE RS-SC following the SA standards development process has an active portfolio of six completed standards, recommended practices, and guides (all are called “standards”). These RS-SC standards are:
{"title":"History of the First IEEE Reliability Standard","authors":"Louis J. Gullo","doi":"10.1109/mrl.2024.3385733","DOIUrl":"https://doi.org/10.1109/mrl.2024.3385733","url":null,"abstract":"The Reliability Society’s Standards Committee (IEEE RS-SC) is an IEEE group developing and sustaining standards for the reliability engineering profession. The IEEE Reliability Society (RS) develops open consensus-based reliability engineering and related engineering standards using the IEEE Standards Association (SA standards development process). The IEEE RS-SC following the SA standards development process has an active portfolio of six completed standards, recommended practices, and guides (all are called “standards”). These RS-SC standards are:","PeriodicalId":517825,"journal":{"name":"IEEE Reliability Magazine","volume":"139 18","pages":"17-19"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281321","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}
Pub Date : 2024-06-01DOI: 10.1109/MRL.2024.3382945
Joanna F. DeFranco
Artificial intelligence (AI) and machine learning (ML) are technologies that are increasingly being integrated into many critical domains such as healthcare, finance, and vehicles. These are all critical systems given their consequences of failure. Therefore, aspects of these systems such as the data gathered to train them need to be representative of the real world. For systems to be trusted by the public in the sense that they will work as intended and will not cause harm, systems should have the characteristics of trustworthy AI as outlined in NIST AI 100-1: valid and reliable, safe, secure, and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. NIST “Artificial Intelligence Risk Management Framework (AI RMF 1.0”), January 2023, https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
{"title":"Themes From an AI and ML Roundtable Discussion","authors":"Joanna F. DeFranco","doi":"10.1109/MRL.2024.3382945","DOIUrl":"https://doi.org/10.1109/MRL.2024.3382945","url":null,"abstract":"Artificial intelligence (AI) and machine learning (ML) are technologies that are increasingly being integrated into many critical domains such as healthcare, finance, and vehicles. These are all critical systems given their consequences of failure. Therefore, aspects of these systems such as the data gathered to train them need to be representative of the real world. For systems to be trusted by the public in the sense that they will work as intended and will not cause harm, systems should have the characteristics of trustworthy AI as outlined in NIST AI 100-1: valid and reliable, safe, secure, and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. NIST “Artificial Intelligence Risk Management Framework (AI RMF 1.0”), January 2023, https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf","PeriodicalId":517825,"journal":{"name":"IEEE Reliability Magazine","volume":"6 9","pages":"42-45"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279673","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}
Pub Date : 2024-06-01DOI: 10.1109/MRL.2024.3389629
M. S. Raunak, D. R. Kuhn, R. Kacker, Y. Lei
Verification of complex software systems is an important, yet challenging task. Testing is the most common method for assuring that software meets its specifications and is defect-free. To claim that software is defect-free and thus reliable, one has to show that it produces the “correct” output or “behaves” according to specification without failing under all possible parameter values and configurations. In the software verification world, this is known as exhaustive testing. For any software of reasonable size and complexity, exhaustive testing is completely infeasible. Thus, during the testing process, a small subset of parameter values and configurations is selected to ensure that the software is producing its output or maintaining its behavior as “expected.” The selected parameter value for one test execution is called a Test Case, and the set of test cases selected for testing a system is called a Test Suite. The essence of software testing, therefore, lies in effective ways of identifying the test cases and building the test suite. Two overarching questions related to this process include: 1) how to select the test cases and 2) how to decide when enough test cases have been selected. Over the years, researchers have proposed different Test Adequacy criteria for answering these two questions.
{"title":"Ensuring Reliability Through Combinatorial Coverage Measures","authors":"M. S. Raunak, D. R. Kuhn, R. Kacker, Y. Lei","doi":"10.1109/MRL.2024.3389629","DOIUrl":"https://doi.org/10.1109/MRL.2024.3389629","url":null,"abstract":"Verification of complex software systems is an important, yet challenging task. Testing is the most common method for assuring that software meets its specifications and is defect-free. To claim that software is defect-free and thus reliable, one has to show that it produces the “correct” output or “behaves” according to specification without failing under all possible parameter values and configurations. In the software verification world, this is known as exhaustive testing. For any software of reasonable size and complexity, exhaustive testing is completely infeasible. Thus, during the testing process, a small subset of parameter values and configurations is selected to ensure that the software is producing its output or maintaining its behavior as “expected.” The selected parameter value for one test execution is called a Test Case, and the set of test cases selected for testing a system is called a Test Suite. The essence of software testing, therefore, lies in effective ways of identifying the test cases and building the test suite. Two overarching questions related to this process include: 1) how to select the test cases and 2) how to decide when enough test cases have been selected. Over the years, researchers have proposed different Test Adequacy criteria for answering these two questions.","PeriodicalId":517825,"journal":{"name":"IEEE Reliability Magazine","volume":"51 14","pages":"20-26"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141275224","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}
Pub Date : 2024-06-01DOI: 10.1109/MRL.2024.3388408
Domenic Forte, Ben Amaba, Cate Richards, Jeff Daniels
Side channels are nonfunctional characteristics of a program or hardware (HW), such as power consumption, electromagnetic radiation (EM), temperature, timing, or memory consumption, that allow one to infer information about the program, software (SW), or HW. Often, attackers take advantage of these side channels to uncover secrets from cryptographic systems, web applications, and more. However, in the right hands, side-channel analysis can also be used for anomaly detection where it has several advantages over traditional solutions.
{"title":"Nowhere to Hide: Monitoring Side Channels for Supply Chain Resiliency","authors":"Domenic Forte, Ben Amaba, Cate Richards, Jeff Daniels","doi":"10.1109/MRL.2024.3388408","DOIUrl":"https://doi.org/10.1109/MRL.2024.3388408","url":null,"abstract":"Side channels are nonfunctional characteristics of a program or hardware (HW), such as power consumption, electromagnetic radiation (EM), temperature, timing, or memory consumption, that allow one to infer information about the program, software (SW), or HW. Often, attackers take advantage of these side channels to uncover secrets from cryptographic systems, web applications, and more. However, in the right hands, side-channel analysis can also be used for anomaly detection where it has several advantages over traditional solutions.","PeriodicalId":517825,"journal":{"name":"IEEE Reliability Magazine","volume":"41 1","pages":"27-34"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279277","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}
Pub Date : 2024-06-01DOI: 10.1109/MRL.2024.3388960
Jon M. Quigley
In reliability engineering, failure mode and effects analysis (FMEA) is a cornerstone review methodology for anticipating and mitigating potential failures [1]. While the core principles of FMEA remain consistent, its successful implementation hinges on several key factors: the scope, upfront preparation, duration and focus of FMEA meetings, and connection to design and process testing. This article delves into these crucial elements and their role in bolstering industry reliability.
{"title":"Optimizing FMEA for Enhanced Reliability","authors":"Jon M. Quigley","doi":"10.1109/MRL.2024.3388960","DOIUrl":"https://doi.org/10.1109/MRL.2024.3388960","url":null,"abstract":"In reliability engineering, failure mode and effects analysis (FMEA) is a cornerstone review methodology for anticipating and mitigating potential failures [1]. While the core principles of FMEA remain consistent, its successful implementation hinges on several key factors: the scope, upfront preparation, duration and focus of FMEA meetings, and connection to design and process testing. This article delves into these crucial elements and their role in bolstering industry reliability.","PeriodicalId":517825,"journal":{"name":"IEEE Reliability Magazine","volume":"87 8","pages":"35-41"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141278096","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}
Pub Date : 2024-06-01DOI: 10.1109/MRL.2024.3386643
Jason Rupe
Chiplet technology is a favored way to push Moore’s law to overcome the chip shrinkage problem, where the smaller scale needed leads to smaller yields. Through chiplets, smaller functional building blocks can be interconnected as needed in flexible ways and scale with new advantages. There are reliability challenges, but they can be overcome with good reliability engineering and practice. The resulting chiplet technology brings new advantages too. With these new advantages comes the potential for greater reliability for the customer. In this column, I explore chiplets, and I invite you to “chip in”!
{"title":"Reliability and Chiplets","authors":"Jason Rupe","doi":"10.1109/MRL.2024.3386643","DOIUrl":"https://doi.org/10.1109/MRL.2024.3386643","url":null,"abstract":"Chiplet technology is a favored way to push Moore’s law to overcome the chip shrinkage problem, where the smaller scale needed leads to smaller yields. Through chiplets, smaller functional building blocks can be interconnected as needed in flexible ways and scale with new advantages. There are reliability challenges, but they can be overcome with good reliability engineering and practice. The resulting chiplet technology brings new advantages too. With these new advantages comes the potential for greater reliability for the customer. In this column, I explore chiplets, and I invite you to “chip in”!","PeriodicalId":517825,"journal":{"name":"IEEE Reliability Magazine","volume":"76 2","pages":"8-11"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141278315","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}
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