Software Reliability

May 4, 2016
FREE RELIABILITY WEBINAR – “Advantages of IEEE P1633 for Practicing Software Reliability”
Host: Ops A La Carte
Speaker: Ann Marie Neufelder, Founder, SoftRel LLC
Date: May 4, 2016
Time: 12:00pm-1:00pm Pacific Time

Software reliability engineering has existed for almost 50 years. Software reliability metrics such as failure rate, MTBF, availability and reliability have been used successfully in industry to plan, manage and demonstrate the achievement of system reliability objectives. The newly revised IEEE 1633 Recommended Practice for Software Reliability provides actionable step by step procedures for employing software reliability models and analyses during any phase of software or firmware development with any software lifecycle model for any industry or application type.

It includes easy to use models for predicting software reliability early in development and during test and operation. It also provides for methods to analyze software failure modes and include software in a system fault tree analysis.

For persons who are acquiring software it provides the ability to assess the reliability of COTS, FOSS, and contractor or subcontractor delivered software.

This presentation will cover the key features of the IEEE 1633 Recommended Practices for software reliability.

To Register and for more info, please call 408.654.0499, x203

March 3, 2016

Food sponsored by I.C.E. Labs, ISO 9001 & 17025 Reliability Test Lab

Title: Physics- based life distribution and reliability modeling of solid state drives
Invited Speaker: Dr. Alexander Parkhomovsky, Ph.D., Engineering Development Manager at Lumentum
Date: Thursday, March 3, 2016
Time: Check in and food at 6:00PM – 6:30 PM. Presentation from 6:30 PM to 7:30 PM
Location: Qualcomm Inc., 3165 Kifer Rd, Santa Clara, CA, 95051 (Meeting will be in the cafeteria, Building B)

Admission: Open to all IEEE members and non-members

Abstract: The model of solid state drive (SSD) life time distribution from physics-based life model considering the random nature of real world customer data usage and product inherent physical properties is developed. The talk is focused on the following two cases:

Case 1: When only field write duty cycle is treated as a random variable while assuming all other physical characteristics are non-random, it is found that the SSD life time follows . Reciprocal-Weibull distribution when field Write Duty Cycle follows Weibull distribution, . Reciprocal-Exponential distribution when field Write Duty Cycle follows Exponential distribution, . Lognormal distribution when field Write Duty Cycle follows Lognormal distribution, . Reciprocal-Normal Distribution when field Write Duty Cycle follows Normal distribution. The corresponding mathematical expressions for reliability, unreliability, hazard rate, MTTF, etc. are derived for each scenario accordingly.

Case 2: In real world, SSD endurance rating is also a random variable due to part-to-part variance from material in-homogeneity and inherent defects from manufacturing process. Given the distributions of field customer write duty cycle (stress) and SSD endurance rating (strength), the distribution of lifetime random variable can be derived either analytically, if closed form solution exists, or numerically using Monte Carlo simulation if no closed form solution exists. This paper provides a special case where the analytic solution exists when both random variables follow Lognormal distribution. A numerical example is given to show the application of the models developed in this paper. The results derived in this paper will benefit the SSD industry in various aspects of product design, development, reliability testing and prediction, field return/failure estimation and warranty management.

For more information and to register, visit: Eventbrite Registration

When we introduce a new chip, we plan and execute a comprehensive reliability qualification plan. This plan will be based on many different reliability stresses addressing infant mortality rate test, early life failure rate test, long term life test failure rate prediction based on a small population of samples pulled from early production lots .
Due to the fact of limited device sample sizes, we are trying to assign a confidence level to our failure rate predictions using “industry standard” chi-square adjustment in the hope, our prediction will be closer to real field failure rates.
This is a “standard” approach of the semiconductor industry because testing very large sample sizes of chips is economically not feasible, especially for small-and fabless semiconductor companies.
IBM Corp.’s Semiconductor Division calls the above practice “finding the tip of the iceberg  “only indicating if there are major catastrophic failure mechanisms”. IBM and major semiconductor manufacturers are stressing large sample sizes in ongoing reliability testing of the outgoing device population.
Above approach requires capabilities and facilities for ORT (ongoing reliabiliy testing) of tens of thousands of devices per year. Only major dedicated manufacturers do this
(like Intel, National Semiconductor, Micron, etc. )
In the course of 2-3 years of intensive ongoing reliability testing of samples of the outgoing population combined with field failure information will one be able to make reliability assessment and meaningful prediction of the maturing semiconductor product.

Function Point Analysis (FPA) has been proven as a reliable method for measuring the size of computer software.  First made public by Allan Albrecht of IBM in 1979, the FPA technique quantifies the functions contained within software in terms that are meaningful to the software users.  It can be readily applied across a wide range of development environments and throughout the life of a development project, from early requirements definition to full operational use. In addition to measuring output, Function Point Analysis is extremely useful in estimating projects, managing change of scope, measuring productivity, and communicating functional requirements.  The Function Point Analysis technique provides an objective, comparative measure that assists in the evaluation, planning, management and control of software production.

The function point measure itself is derived in a number of stages. Using a standardized set of basic criteria, each of the functions is a numeric index according to its type and complexity. These indices are totaled to give an initial measure of size which is then normalized by incorporating a number of factors relating to the software as a whole. The end result is a single number called the Function Point index which measures the size and complexity of the software product.

There are many benefits in using Function Point Analysis:

  • Function Points can be used to communicate more effectively with user groups.
  • Function Points can be used to reduce overtime.
  • Function points can be used to establish an inventory of all transactions and files of a current project or application.  This inventory can be used as a means of financial evaluation of an application.  If an inventory is conducted for a development project or enhancement project, then this same inventory could be used to help maintain scope creep and to help control project growth.  Even more important this inventory helps understand the magnitude of the problem.
  • Function Points can be used to size software applications. Sizing is an important component in determining productivity (outputs/inputs), predicting effort, understanding unit cost, so on and so forth.
  • Unlike some other software metrics, different people can count function points at different times, to obtain the same measure within a reasonable margin of error. That is, the same conclusion will be drawn from the results.
  • FPA can help organizations understand the unit cost of a software application or project.

Once unit cost is understood tools, languages, platforms can be compared quantitatively instead of subjectively.

Further information can be found at