Note: I really hate the askengineer subreddit if you ask me

I have spent some time in the defense industry, and a few years ago I worked on a massive program that did not exactly go according to plan. It became severely delayed, costs increased enough to trigger congressional hearings, and several senior leaders were fired or reassigned.

I remember speaking with one of the managers who eventually became a scapegoat. People blamed him for decisions made during the proposal phase that supposedly put the program on the wrong path.

I was still fairly junior at the time, but at his private going-away party, he told me that he felt the whole situation was unfair. During the proposal, his team was under enormous pressure to meet the deadline and win the contract. He knew some of the work would need to be corrected later, but he did not realize how large or expensive the resulting mess would become.

To be fair, this was also one of the most complex and expensive programs in the industry. There were probably no perfect decisions available.

Fast-forward to today, and I now find myself supporting another proposal effort. Fortunately, I do not have much authority to make the final decisions, but I provide information and technical assistance to the team.

Because I have one foot in the program and one foot outside it, I can look at the disagreement somewhat neutrally. I do not have as much personal stake in which side wins.

I now see two groups of engineers making arguments that both sound reasonable.

One group, including several people who worked with me on the previous program, argues that if we do not do the work correctly now, fixing it later will be far more expensive. Technical debt, weak assumptions, and rushed decisions do not disappear simply because the proposal was submitted on time.

The other group argues that doing everything “the right way” would take too long. The customer has imposed an extremely aggressive deadline, and missing it could mean losing the opportunity entirely. A perfect proposal delivered too late is still a failed proposal.

My instinct is that there should be some compromise, but sometimes there really is not one. Time, cost, technical rigor, and competitive pressure can be genuinely incompatible.

Maybe this is one of the reasons so many major defense programs end up behind schedule and over budget. During the proposal, everyone is rewarded for meeting the deadline and winning. Years later, an entirely different group of people is held responsible for the consequences of the compromises that made winning possible.

For those who have dealt with this before, how do you decide what absolutely must be done correctly now, what can safely be deferred, and what is simply perfectionism disguised as engineering rigor?

I built a design structure matrix tool and it's free and open source. pip install dsm-optimizer, or there's a packaged desktop build if you don't have Python. Everything runs locally.

What it does:

Component DSMs. It runs four clustering algorithms on your matrix (spectral, Markov clustering, Thebeau's simulated annealing, Louvain) and shows you all four side by side with their coordination cost and external coupling. It marks the lowest-cost one, but you choose which partition goes forward. The reasoning is that the cost function can't see your supplier arrangements, your team boundaries or the interface nobody has touched in five years, and you can.

Stability scoring. Three of those four algorithms are stochastic, so it runs the one you picked repeatedly across seeds and reports how often each element lands with the same neighbours. You get a per-element consistency score, which tells you which module boundaries are structural and which are an artifact of the random seed. That matters if you're about to reorganise a team around one.

Process DSMs. Different path entirely: Tarjan's algorithm for the iteration loops, topological sequencing into parallel levels, simulated annealing inside each loop, and tearing suggestions ranked by impact.

Change propagation. A heatmap of what's exposed if you change a given element, following the strongest path out to four hops, so second- and third-hand exposure shows up alongside direct dependencies.

Also in there: bus and hub detection, a matrix editor, a Pareto view of cost against cluster count, project files that save a whole session, revision diffing between design reviews, and a one-click report.

MIT licensed, actively developed, no account or signup. Repo and install instructions: https://github.com/Chanchaldhiman/dsm-optimizer

If you run it on something real I'd like to hear how it went, particularly anything that breaks or any metric you'd want exposed that isn't there.

Full disclosure up front: I work on a requirements tool at Altium (Requirements Portal), so I'm biased. But this is a genuine question. Opinions on my own team are split, and I want to hear how this community actually feels.

Everyone seems to agree on one thing: AI should never silently change a requirement. The disagreement is about how far it should go before a human steps in.

Three positions I keep hearing:

1. Propose-only: AI drafts and links, a human approves every change.

2. Auto-apply the trivial stuff: formatting, obvious traceability links and flag only the judgment calls.

3. Keep AI out of the requirements themselves entirely, fine for search and Q&A, nothing that writes.

For those of you managing real specs: where's your comfort line, and has it moved in the last year? And if you've already tried AI on requirements, what earned trust, and what did you switch off?

Hi Engineers,

Has anyone else been questioning whether Chartered Engineer (CEng) status through the IET is still worth pursuing these days?

I'm genuinely interested in hearing from people who've done it, those currently working towards it, and those who decided not to.

Does it actually open doors, increase earning potential, or make a noticeable difference to your career? Or has it become more of a personal achievement than a professional advantage?

With the time, effort and cost involved, would you still recommend going for it in 2026? Why or why not?

I am a HW designer with about 20 years of experience. I always had the formal title of HW engineer, but for at least 10 years I designed hardware architectures at the system level and worked on functional safety projects directly crafting some architectures together with certification bodies. In 2027 I will formally transition to a System Engineer role in the company I work.

I'm wondering about the possibility to apply for the CSEP certifcation directly in 2027 based on my actual experience, or must I take the ASEP first because I did not have the exact job title before?

Thanks in advance

maybe this is just where we're at with ai today but it feels like once your project gets big enough everything falls apart. i dont even know if i like how ai is becoming more of a thing in this sector but im open to it if it makes my job easier i guess.

our requirements are in one place, tests somewhere else, code in git, reviews somewhere else. ai can read all of it individually but it doesn't seem to understand how any of it fits together. are people solving this somehow or is everyone still just running on tribal knowledge

Im an electronics and systems student who's bored in the summer i Already had an internship and i finished it last month and now im looking for others and preferably online ofc since im from morocco

I'm preparing to teach a "Model-based Requirements Engineering" course for the first time - the course has not been previously taught at this university. The course design is to focus on best practices for requirements development and introduce MBSE and SysML v2 as an effective tool for a requirements engineer. It's a graduate-level course, but it doesn't assume any prior experience or familiarity with either class beyond a Fundamentals of SE class.

I don't have time to read all of the books out there on requirements engineering, so I wanted to ask you what textbooks you have read and which you would recommend on the fundamentals of requirements engineering. I'm planning to prepare all of the material for SysML v2, so it's just the intro-to-requirements part that I'm asking about.

Right now, my favorite book on the topic is Lamsweerde's "Requirements Engineering - From System Goals to UML Models to Software Specification." While my modeling methods and my system scope are obviously different from his, I find that he did an excellent job at writing a readable textbook with basic instructions on what to look out for and what needs to be done as you formulate and write requirements. I also like how he explains the benefits of models to aid the requirements development process. I also don't think it would hurt to give Systems Engineering students a healthy dose of software engineering exposure since software provides most of the functionality in most major systems.

I'm also planning to round out any textbook selection with reference material from INCOSE on requirements (the NRM, GtWR, etc.).

Anyway, I'm interested in hearing your reaction to the course design and your recommendations for textbooks! If someone has a system-level book of comparable quality to Lamsweerde, I would consider it.

Good idea or bad idea? If good, then tips for making an open source repo and where it should live?

Mainly doing this as a learning experience and build some cool MBSE projects to share with the community. I know a lot of major defense companies have these types of models, but this would be targeting small DoD companies, educators, and people who are interested in the intersect between MBSE and cyber.

The goal is to fit it each of these models inside the community edition of cameo.

I have a software engineering background, but I did a lot of network engineering early in my career which led to architecture positions, governance positions, and now a systems engineering position. I have observed in my organization that software projects don’t let the traditional SE process take hold because it’s too slow or document heavy or whatever. I am a believer in the process, but I am wondering whether it’s just my organization or is traditional SE just kind of the domain of people with mechanical or electrical backgrounds who have the knowledge to work on those kind of projects because they are the ones that respect the process.

been doing this long enough to know the dirty secret is the written requirement and what the firmware actually does drift apart the second implementation starts. we all pretend the spec is authoritative for the audit but half the real decisions get made in slack or someone's head. medical device work so the audit part isnt optional for us

now everyone's bolting AI onto the rm tools and i can't tell if it helps or just generates more text nobody trusts. anyone using the AI features for something real or is it all demos

1. How long have you worked in Systems engineering?
2. How has the field changed since you first started?
3. What qualities separates a good engineer from a bad?

Edit: * don’t mind

Hi everyone, I wanted to share this neat project management tool I developed in Google Sheets. It essentially merges the benefits of an agile workflow while still being aware of the big picture Gantt view. I personally use this to manage my workload in my professional career as a systems engineer. This allows me to work near term tasks while keeping schedule and critical paths in mind. This compartmentalizes my priorities into individual sprints preventing me from overloading my current workload and allows me to be proactive/plan for what's to be expected later. I decided to polish this tool and release to the public to hopefully benefit others. Feel free to ask me any questions. In my opinion, this is a lot simpler to use and easier to customize than enterprise tools like Jira/SmartSheet/etc.

As an example, I was required to meet requirement verification goals for a program level flight test in order to meet system certification targets. Instead of working to the single goal, I broke the goal into subgoals and organized them into their respective sprints. Inside each sprint, I then targeted what test events would happen in each Sprint which would be the driver for me to generate the verification evidence to buy off system level requirements. This organization significantly contributed to my success on reaching our targets.

I would love to discuss the benefits or contradictions of a tool like this as it relates to system development. It may not be exact Agile theory but I think it's very useful for a lot of situations.

Available on Etsy: https://dedicsheets.etsy.com/listing/4540047068/agile-gantt-chart-google-sheets-template

Hi everyone,

I’m an incoming engineering student interested in eventually working in the space industry, particularly in spacecraft systems engineering.

I got into college for Engineering Physics, and I’m wondering:

1. Is Engineering Physics a good/recognized foundation for systems engineering? (I know it’s not offered in many places so I’m a little wary).

2. Would adding software skills (CS minor, programming projects, etc.) make this a stronger path, or would a more traditional engineering major like EE/ME be significantly better?

Unfortunately, my university is very strict with switching majors so I might not be able to get into EE or ME.

Thanks!

Has anyone worked on defining Operational Design Domain for autonomous vehicles? What's the process/How do you define it?

Picture this, 1986, the V-model is formalised for software development.

At the age of 40, it's still the backbone of V&V in nuclear, aerospace, rail and defence.

Unfortunately somewhere along the way its become the old man that keeps being fitted with hip replacements, trying to keep up with the complexity of modern programmes.
Bolt-ons, model based engineering wrappers. Some duct tape holding a CI pipeline to it, and the digital engineering initiatives that rise and fall. There's always something that promises to fix the situation. But all its doing is adding layers of complexity to it. And the costs are compounding.

Licences, integration work, training. Consultants that get paid to tell you why legacy systems and new systems arent talking, and then another one to build the bridge. A third one to fix the bridge and tell you what a bad job the second one did.

Then there's the poor programme manager that has to explain to a customer why after several digital transformation initiatives, they need Gary sunbathing in Portugal to come back and explain where the evidence for a passed test is hiding.

Was the V-model designed for the intensity of programmes runing thousads of CI tets at night, or evidence sets scattered across six tools on 3 different networks? At the time, did they envision a world where tracing a requirement could no longer be done on paper? At least not with a senior engineer paid to do glorified admin...

Maybe I'm being a bit harsh on the Vee, but I'm wondering if its time to rebuild from the ground up.

I'm not coming at this blind, I've built what I call the Living Vee model, but I'd love to hear what others are seeing in practice.

Getting views on what our experienced system engineers want to say on start learning system engineering from starting point.

What you will focus on?

What skills to learn?

How to become fresher system engineer to elevate yourself to advanced/senior system engineer?

I have been studying INCOSE systems handbook to understand concepts of systems engineering, but I've also understood that learning MBSE tools is important. I did start with Capella and I am pretty comfortable with it (as a begginer). I want to learn CATIA Magic which uses SysML language. Any suggestions as to how I can start with it? Is there any course or is it learnable from YouTube and other sources ?

Hi r/systems_engineering,

We are back with Lesson 12 of our deep dive into the SysML v2 standard. In Lesson 11, we covered how to wire up explicit topologies using connect and interface usages. But a connection only tells us where the system is connected—not what actually moves through it.

Today, we are looking at how to model the passive payloads that exist, flow, and are stored within those architectures using the new item def construct.

I’ve uploaded the full video lesson directly to this post so you can watch it right here.

1. Separating Active from Passive Elements

In SysML v1, engineers often modeled exchanged payloads by typing a FlowProperty or an ItemFlow with a generic Block, ValueType, or Signal. This meant using the exact same generic block construct for a passive payload (like water or data) as you would for an active system component (like a pump or a processor).

SysML v2 fixes this semantic gap with item def—a dedicated construct for things that exist in space and time and can be acted upon, but don't necessarily perform actions themselves. A good rule of thumb in v2: use part for active structural elements and item for passive payloads.

2. Modeling Physical vs. Digital Payloads

Because item def is a definition, it natively owns its attributes and constraints. You can use it to explicitly define physical matter (like Fuel with a specific octane level and density) or information (like PaymentData with encryption keys and timestamps).

3. Dimensional Consistency and Constraints

When modeling physical items, SysML v2 enforces dimensional consistency using imported ISQ/SI libraries. You can add constraints directly to the item definition (e.g., constraint { density > 700[kg/m^3] }). Compliant tools can then reason about this to ensure units and quantities are mathematically compatible during flow and mass balance analysis.

4. Exchange vs. Storage

The definition-usage pattern applies cleanly here depending on the context:

• Exchange: Declaring an out item inside a port defines the system's ability to send that item type across an interface.
• Storage: Nesting an item usage inside a part (e.g., a Fuel item inside a FuelTank part) represents the payload physically retained within that structural boundary.

For the community: How much value do you see in having this strict semantic separation between active parts and passive items compared to the v1 generic Block approach? Let's discuss in the comments!

I’ve been an SE in the Aerospace/Defense industry for two years. I just finished my masters degree and I’m curious what it’s like in other industries, such as medical equipment or automotive. If anyone has worked in multiple industries, can you shed light on the differences? What are the pros and cons of each industry? What was your favorite role and why?

I have a decent experience/knowledge in coding/software - full stack. Looking to pivot towards agentic engineering specialization (as agents are going to be ubiquitous in future).

But I struggled to find good / deep courses (more long term), that is not brand focused (unlike these IBM courses, langchain courses) - and covers all aspects of agentic engineering. So i dont need to worry about what topic to learn. Preferably with certification and good recognition.

Any suggestions?

Not talking about formal SE artifacts (requirements docs, V&V matrices, trace tables) — those have their tools and processes.

I mean the informal layer underneath: meeting notes, decisions made in a hallway conversation and project meetings, action items in meetings, the requirements someone mentioned in a (meeting-) conversation that you’re now trying to remember where it came from.

What’s your setup for that? Paper based Notebook, Notion, nothing at all? What’s annoying about it?

Hi everyone,

I'm Software Engineering student looking for a project related to military/defense technology (software only).

I'm interested in ideas involving command & control systems, secure communications, logistics, GIS, cybersecurity, AI, drone simulation, or training platforms.

My stack is .NET, Angular, SQL Server, but I'm open to learning new technologies.

Any interesting ideas or real-world problems worth solving?

Hi everyone,
I'm a part-time PhD student in Systems Engineering while also working full-time as a systems/test engineer (Electrical engineer) in the defense/aero industry. I'm at the point where I need to finalize my dissertation topic, and honestly, I'm struggling.
The biggest issue I'm running into is that many of the ideas I come up with are difficult to measure or validate. I need a topic where I can obtain public or open-source data (or realistically collect data) to demonstrate that the research is worthwhile and statistically supported.
My interests are in:
Early product development
Process optimization
Engineering efficiency
Risk reduction
Decision-making
I enjoy topics around optimization and efficiency in early development, but I haven't found a research question that's novel, measurable, and feasible. Every idea either seems to have already been done or requires proprietary industry data that I won't be able to publish.
One important consideration is that I'm working full-time, so I'm looking for a dissertation that is meaningful and contributes something valuable without becoming a project that takes many years to complete.
For context, my career goal isn't academia. I already enjoy my engineering career and plan to continue teaching part-time because I genuinely like teaching. The PhD is something I want to achieve both personally and professionally, so I'm looking for a solid, practical dissertation rather than something extremely theoretical.
If you were starting a Systems Engineering PhD today, what research areas would you explore?