2B0-102 approach - Enterasys Security Systems Engineer-Defense Updated: 2023

Originally Published MDDI January 2002

VALIDATION

A Systems Engineering Approach to Requirements Validation

Product development risks can be reduced by validating project requirements before the design process begins.

Trace Baker

Measured in money, time, safety, and reputation, the errors that are most costly to manufacturers of medical devices are often those made early in the product development cycle. One such error—committing resources in an attempt to meet product requirements that later prove to be invalid—can be prevented by conducting a requirements validation.

Although design input is a focus of the design control sections of the quality system regulation (QSR), most of the literature on medical device development treats requirements review only superficially. There are, however, techniques developed by systems engineers for other industries that device companies can adopt. Modeling, in particular, has proven extremely effective for ensuring the validity of product requirements.

FDA CRITERIA

Unlike design validation, which is performed after a design has been implemented, requirements validation is performed before the design process starts. The relationship of requirements validation to a typical medical product development process is shown in Figure 1, which was adapted from FDA's design control guidance.¹ FDA's guidance also offers five criteria for "assessing design input requirements for adequacy"; specifically, requirements must be unambiguous and verifiable by an objective method of analysis, inspection, or testing; include quantitative limits; be self-consistent and nonconflicting; characterize the use environment; and comply with relevant industry standards. The first three items concern the requirements' validity, the last two their completeness.

It is easy to create examples of requirements that are obviously invalid under the first three criteria. Consider the statement "The deionized-water container shall have a capacity of 1 L and the weight of the filled container shall not exceed 1 kg." A liter of water weighs about 1 kg, so there is no budget left for the weight of the container. Thus, the requirements clearly conflict. Similarly, the requirement that "the instrument shall be capable of fitting through a door" is clearly ambiguous. Adding the dimensions of the smallest door through which the instrument must fit would remove the ambiguity.

Like the FDA guidance, the literature on systems engineering also emphasizes the need for eliminating inconsistency and ambiguity from requirements specifications.^2–4 Medical device manufacturers may find an article in the Handbook of Systems Engineering and Management particularly helpful.⁵ It defines the requirements validation process as ascertaining the answers to three questions:

• Is the set of requirements consistent?
  
• Can a practical system be built that satisfies all of the requirements?
  
• Is it possible to prove that the system satisfies the requirements?

A CASE STUDY

The systems engineering approach to requirements validation is also exemplified in the following case study, which involves the development of a fluid-delivery subsystem for a diagnostic device. To use the device, a laboratory technician places each patient demo in a separate well of an eight-well block, which is then handled as a unit. Four requirements, R021–R024, were suggested to specify the delivery to the wells of the reagent needed to process the samples:

• R021—The nominal dispensed volume (v) of reagent in each well shall be 60 µl.
• R022—The maximum error (e) of reagent dispenses shall not exceed ±2% when tested across 30 consecutive wells, as calculated by the formula

• R023—The coefficient of variation (c) of reagent dispenses shall not exceed 1% when tested across 30 consecutive wells, as calculated by the formula

• R024 —Dispenses of reagent into all wells in a single block of eight wells shall meet the error requirement R022.

These requirements use the common definitions for the demo meanand standard deviation (s) for a demo size of 30:

The need for these requirements is understandable. Because variation in performance is inevitable in a physical system, limits to that variation have to be defined by what the chemistry can tolerate and still produce correct results.

Common medical industry practice since the implementation of design controls is to collect requirements, write a requirements document, and then conduct a requirements review. Would this set of hypothetical requirements pass a review using the first three items listed in the FDA guidance? All four requirements can be tested by simple gravimetric measurement or by inspection, and all four include quantitative limits. They also appear consistent; each addresses a separate aspect of fluid delivery, and they are related to one another through a series of equations. Finally, if there is conflict among the requirements, it is not immediately evident. It seems logical that if the subsystem can meet error requirement R022 for a series of 30 consecutive dispenses, it will be capable of doing so for the eight consecutive dispenses demanded by R024. However, using a systems engineering approach to requirements validation reveals that this assumption is not necessarily true.

A systems engineer might begin validating this set of requirements by posing the question, Is the proposed fluid-delivery subsystem capable of meeting requirement R024 given that it can meet R022 and R023? He or she would then attempt to determine the answer by constructing the simple mathematical models of the three requirements described below. (These analyses are based on two assumptions: that the dispenses are statistically independent, and that dispense error is distributed normally.)

The first model considers a limited case where the subsystem is operating with a consistent level of dispense error and coefficient of variation that fall within the R022 and R023 requirements: specifically, an error of +1.5% and a coefficient of variation of 0.8%. With the constant error of +1.5%, the average dispense volume will be 60.9 µl, which is high but still within specification. This case is shown graphically in Figure 2, with the height of the curve representing the probability that any single dispense will have the volume given on the abscissa. The shaded area under the curve is the probability that the volume dispensed in one well will meet the error requirement in R022. For the combination in this example, the probability of eight consecutive wells meeting R022 is 0.97⁸, or approximately 0.78, meaning that as many as 22 of 100 tests attempted may fail to meet this requirement because of the random variation that is allowed by R023. This level of performance is clearly unacceptable for a diagnostic device.

The general case that covers the entire specified range of parameters allowed under R022 for error and R023 for coefficient of variation can also be modeled. In Figure 3, this range is enclosed by the box, and the probability that the subsystem will meet requirement R024 at any allowed combination of error and coefficient of variation is shown by the colored surface within the box. If the subsystem were capable of meeting R024 under all specified conditions, the probability would be 1 over the entire boxed-in area and the top surface of the box would be a uniform color. As it is, the surface lies below the required probability over much of the area.

The models reveal that, in this set of requirements, R024 is not valid, and there is a simple explanation for this result. Requirement R024 states that "all wells in a single block must meet the error requirement R022." Since every well is part of a block, the unintended requirement is that every well in the device must meet the ±2% error requirement. This is inconsistent with R022, which was formulated to apply to a demo of 30 wells, not to individual wells. Can a practical system be built that operates only in the colored area of the response surface? Perhaps, but at what price?

IMPLEMENTING REQUIREMENTS VALIDATION

If the requirements validation modeling described in the case study had not been performed, the consequences of accepting the set of four requirements for the diagnostic device's fluid-delivery subsystem probably would not have been discovered until design validation or production testing. The cost would have been extensive rework and revenue lost to delays in introducing the product to the market. Although requirements validation itself may seem like a time-consuming exercise, device manufacturers can add it to the product life cycle without sacrificing rapid development as a project goal by heeding the following advice.

• Validate requirements as they are elicited; do not wait for the review that separates the requirements phase from the design phase of a project.
  
• Model system behavior mathematically before building physical prototypes and running experiments. The example in this article was modeled in less than an hour using Mathcad.
  
• Begin the validation process with simple models—perhaps nothing more than a sketch—to identify areas of concern that require further exploration.
  
• Don't hesitate to consult certified when validating requirements that involve domain knowledge.

A checklist for preparing requirements documents appropriate to medical device development can be compiled for a project by consulting systems engineering books and articles.^2–9 In the demo checklist that follows, the questions apply to both the entire set of requirements and to individual requirements.

• Does the requirement meet a stated stakeholder need?
• Does the requirement preserve the product's competitiveness?
• Is the requirement both necessary and sufficient?
• Is the requirement understandable without having to analyze the meanings of words?
• Does the requirement have a unique interpretation?
• Do all project participants interpret the requirement in the same way?
• If assumptions were made during project definition, is the requirement consistent with those assumptions?
• Is the requirement redundant?
• Does the requirement conflict with others?
• Does the requirement contain errors of fact?
• Is it physically possible to meet the requirement using existing technologies?
• Can the requirement be met within the approved schedule and budget?
• Is the statement of the requirement expressed only in terms of what and why, rather than how?
• If the requirement may have to be changed, is there enough information available to allow the change to be made completely and consistently?
• Does the requirement include specific tolerances?
• Is the statement of the requirement verifiable through modeling, simulation, analysis, inspection, or logical argument?
• Can the realization of the requirement be Checked by a finite, objective, and ethical process?
• Are mandatory requirements distinct from preference requirements (also called goals)?

CONCLUSION

Following a systems engineering approach to requirements validation compels developers to address more issues than the five criteria given by FDA for assessing design input adequacy. Yet the benefits of such increased scrutiny make the expenditure of time and effort worthwhile. By validating requirements before design begins, manufacturers can guard against the need for unexpected rework late in the development cycle or after the product enters the marketplace. The resulting savings in time and money—and even reputation—can be significant.

REFERENCES

1. Design Control Guidance for Medical Device Manufacturers (Rockville, MD: Food and Drug Administration, 1997).

2. I Sommerville and P Sawyer, Requirements Engineering (Chichester, England: Wiley, 1997).

3. BP Douglass, Doing Hard Time (Reading, MA: Addison-Wesley, 1999).

4. RS Pressman, Software Engineering, a Practitioner's Approach (New York: McGraw-Hill, 2001).

5. AT Bahill and FF Dean, "Discovering Systems Requirements," in Handbook of Systems Engineering and Management, eds. AP Sage and WB Rouse (New York: Wiley, 1999).

6. Standard for Application and Management of the Systems Engineering Process, IEEE Std 1220-1998 (New York: Institute of Electrical and Electronics Engineers, 1999).

7. Systems Engineering Handbook, release 1.0 (Seattle: International Council on Systems Engineering, January 1998).

8. [Sub]system Requirements Analysis Phase, JPL D-4003, version 3.0 (Pasadena, CA: Jet Propulsion Laboratory, 1988).

9. Systems Engineering Handbook, vol. 1, MSFC-HDBK-1912 (Huntsville, AL: Marshall Space Flight Center, 1991).

Trace Baker works for Colorado MEDtech in Boulder, CO, where he is a senior principal systems engineer.

Copyright ©2002 Medical Device & Diagnostic Industry

[MirageC] is a bit of a contrarian. Instead of taking pictures of 3D printed objects that show them in their best light, he takes pictures that show them at their worst. The reason? He wanted to figure out why he was seeing a strange artifact in his printer when using a direct extruder. Just at a quick glance, you might think the problem was Z wobble, but, in this case, it was something else. You can see the fine detective work in the video below.

There were a few odd things about the problem. First, it scaled with the part size. Secondly, the problem got better when he switched to a Bowden tube setup. We don’t want to supply away the ending, but you can guess from that clue that the problem had something to do with the extrusion system.

The resulting analysis led [MirageC] to work with BMG to create a special gear which — surprisingly, didn’t help as much as he thought it would. However, it did help point the way to the correct solution.

Along the way, you can learn a lot from following along, and maybe you’ll even Boost the quality of your prints. We always enjoy these detailed analyses of printer issues, like the ones from [Stefan], for example. If you want to go hardcore engineering on your 3D prints, you can always do finite element analysis on your infill.

Software engineering is the branch of computer science that deals with the design, development, testing, and maintenance of software applications. Software engineers apply engineering principles and knowledge of programming languages to build software solutions for end users.

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Thanks to strong industry demand and their own technical expertise, skilled software engineers are well compensated for the value they deliver. The mean annual salary for software developers was $132,930 in May 2022.

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Source: U.S. Bureau of Labor Statistics

More about computing salaries.

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Software developers design software to help solve problems faced by real people. This requires a combination of both technical and soft skills. A bachelor's degree in computer science, software engineering, or a related degree program is the most common entry-level requirement for software engineers.

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With our ever-increasing dependency on technology and the growing internet of things, the future of software engineering is bright. Software engineers are employable in nearly every industry, in both large and small organizations.

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Source: US Bureau of Labor Statistics

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Opportunities to explore software engineering outside the classroom are numerous at Michigan Tech. You might choose to join a computing-focused Enterprise team or student organization, seek out research opportunities with faculty members, or develop an independent project. With an entire College dedicated to computing, you're sure to find your people and form lasting connections while exploring your passion for computing.

When you graduate from Tech, you'll be in demand by employers. 100 percent of our software engineering graduates are employed within six months of graduation. Their employers include major companies such as American Express, Argonne National Laboratory, Boeing, Blue Cross Blue Shield, General Electric, Google, Lockheed Martin, Motorola, Texas Instruments, the US Air Force, and more.

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If you're still deciding which computer science focus you want to pursue, the first-year undergraduate General Computing program gives you one or two semesters to explore the discipline and decide which degree program sparks your curiosity the most. It's a starting point to supply you some space to choose the computing field that fits you the best.

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During Nick Nurse’s five years as head coach of the Toronto Raptors, a hallmark of his teams, which ranged from great to good to middling, was a consistent place among the league’s top 10 in defensive turnover rate. According to Cleaning The Glass, they led the league in that category the past three seasons, were second in 2019-20 and ninth in 2018-19. Contrastingly, the Philadelphia 76ers have only finished top 10 once since 2017-18 – third in 2020-21 – and generally adhered to conservative defensive schemes under former head coaches, Doc Rivers and Brett Brown.

Recently, the Raptors’ brash style was partly borne out of necessity. They often lacked a true rim protector, so forcing turnovers was the path to successful possessions that kept the ball out of the paint. Their half-court offense was sticky, so generating fast-break opportunities helped avoid that stickiness. Philadelphia, however, has its anchor in Joel Embiid and should be a pretty good offense with or without James Harden.

Personnel helps dictate schemes and this is a completely different roster composition than those Toronto squads. Even so, these Sixers seem well-equipped to continue some of the aggression Nurse gravitated toward up North.

De’Anthony Melton, Patrick Beverley, Tobias Harris, Kelly Oubre Jr. and Paul Reed are all dexterous defenders touting gaudy playmaking rates who should be empowered to hunt takeaways. Melton and Beverley excel in pressuring at the point-of-attack to render opponents uncomfortable and ideally produce mistakes. Harris and Oubre love the sneaky swipe as their man transitions into a shot or pass. Paul Reed has magnets for hands and is tremendously good trapping ball-screens in pick-and-roll coverage.

All of them placed in the 61st percentile or better at their positions in steal percentage in 2022-23. Reed and Melton were above the 90th percentile in both steal and block percentages. Melton’s boom-or-bust approach sometimes rattled possessions when it failed because of the team’s typically conservative game plan. Entering the year with an aggressive ethos could make it easier for everyone to adapt when Melton’s pursuits fall flat.

Given Embiid’s enormous offensive responsibilities and possession-by-possession inconsistencies defensively, tabbing him as the safety blanket of a high-flying unit may be putting too much on his plate. He’s a very good defender, but someone whose peak on that end has declined from the early days of his career, which manifests with some unpredictability in his commitment to rim protection.

If Tyrese Maxey proves ready to assume a larger workload offensively, Harris sees an uptick in usage, particularly as a creator, and Oubre is garnering notable volume, it’s not inconceivable Embiid could be more of a 27- or 28-point scorer rather than a 33-point one and is ready to dial up the defensive intensity. I expect he’ll be tasked with more facilitating duties in lieu of Harden’s presumed absence and that may counteract any scoring decline, but there’s a world in which he rediscovers his All-World mojo defensively. And if Nurse has his way, that will certainly be the case.

“I’m going to really expect a lot more rim protection from him. I would say that would be where I’m going to start at,” Nurse said during an appearance on “The Pat Bev Pod” earlier this month. “We’re going to probably let our aggressive guards be really aggressive and funnel a lot of things to him. I am expecting that, I always say like, he’s gonna take more swings at blocked shots.”

Under Rivers, Philadelphia was pretty tepid in providing help at the nail and one pass away on drives. Between the Raptors’ habits and his words above, that likely changes under Nurse, who also said Reed will “for sure” play alongside Embiid. Through three seasons, they’ve only shared the floor for 25 possessions, so it’ll require an adjustment period. The offense will stall at times, with Reed’s long-range shooting a question mark. He’s a much better cutter and finisher than P.J. Tucker -- who defenses generally ignored next to Embiid last season -- so the big fella’s gravity and passing prowess may help navigate the Sixers through some of those lulls.

Defensively, though, the possibilities are pretty intriguing. Assigning Embiid to poor or non-shooters and letting him park near the rim, while Reed causes havoc in pick-and-rolls, could spur quite the dilemma for opposing offenses. The projected rotation (Maxey, Melton, Harris, Tucker, Embiid, Beverley, Oubre, Reed) is more defensively slanted than last season. Those upgrades specifically align with Nurse’s schematic preferences and it could help the Sixers establish a defensive identity that generally escaped them for the majority of last season.

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Meta’s Approach to Protecting Teens Isn’t Working, Says Former Engineer

A former Meta employee says executives at the company are failing to protect minors on its platforms. WSJ reporter Jeff Horwitz joins host Julie Chang to discuss what led him to testify on Capitol Hill. Illustration: Taylor Callery

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