ASQ RRD series webinar: Introduction to Software Reliability

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ASQ RRD Series Webinar: Introduction To Software Reliability

Presenters: Drs. Lance Fiondella and Vidhyashree Nagaraju

Thursday, September 12, 2024 12:00 PM – 1:00 PM

https://asq.webex.com/weblink/register/re67874ad2a549c4b1a046605446d08c9

Abstract:

Modern commercial and defense systems increasingly depend on software for their functionality. Unlike hardware, software does not wear out physically, as it is not directly subject to any mechanical or electrical stress throughout its lifetime. However, while hardware has enjoyed significant gains in reliability in recent decades, many systems have attributed a disproportionate number of failures to software. Thus, software plays an outsized role in system unreliability, severely detracting from system design targets. One reason for this increase in software failures is the growth in the size and complexity of these software applications to millions of lines of code, which makes exhaustive testing infeasible. In the absence of methods to ensure that software is free of faults, researchers have developed models based on probability and statistics to quantitatively demonstrate that software reliability improves as faults are detected during testing and removed as well as to promote efficient resource allocation during software testing.

This talk presents an introduction to software reliability and a widely applied class of software reliability models, namely failure counting models along with recent extensions. We provide a review of prerequisite concepts from reliability as well as numerical examples to provide attendees with a deeper understanding of the model application process. In addition, the application of simple and robust tools such as Software Failure and Reliability Assessment Tool (SFRAT), which is an open-source tool that enables application of software reliability model without needing to learn the underlying mathematics.

Who should attend: This presentation is suitable for researchers and practicing software, reliability, and system engineers and managers responsible for the design, test, or acquisition of software-intensive systems or systems that interface with software-intensive systems.

Pre-requisites:

· No pre-requisite knowledge of mathematics, statistics, or programming is required.

Take-aways:

· Understand software reliability and importance of software reliability growth models in SDLC.

· Understanding of the model application process.

· Application of tools such as SFRAT and ability to interpret results.

Biography:

Vidhyashree Nagaraju is a Senior System Safety Engineer at Edge Case Research Inc. (ECR), PA USA. Prior to joining ECR, Dr. Nagaraju consulted as a Software Safety and Reliability Engineer IV at Collins Aerospace to support a NASA ISS project. She has also worked as a tenure-track faculty for two years at the University of Tulsa in the Department of Computer Science and briefly at Stonehill College. She has published over 40 peer-reviewed journal articles and conference papers on reliability modeling and algorithms in journals such as IEEE Transactions on Reliability and Journal of Risk and Reliability. Her research has been recognized with student paper awards, including the 2020 Tom Fagan Student Paper Award from the IEEE Annual Reliability and Maintainability Symposium (RAMS).

Lance Fiondella is an associate professor of Electrical and Computer Engineering at the University of Massachusetts Dartmouth and Director of the University of Massachusetts Dartmouth Cybersecurity Center, an NSA/DHS-designated Center of Academic Excellence in Cyber Research (CAE-R). His research has been funded by the Department of Homeland Security, NASA, U.S. Department of Defense, and National Science Foundation.

Dr. Fiondella served as the vice-chair of IEEE Standard 1633: Recommended Practice on Software Reliability from 2013-15 and a three-year term as a Member of the Administrative Committee of the IEEE Reliability Society from 2015-2017. He presently serves as the advisor to Working Group 35 on AI & Autonomy of the Military Operations Research Society.

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