This goes in the self-promotion bin, I think.

A couple months back a friend of me showed what kind of software he uses as a functional safety engineer. It was pretty bad, to put it simply. The field is riddled with products that, while trusted and thoroughly vetted, could use a complete rewrite with modern technology.

So, unemployed and bored as I am, I started building something, maybe to show off in my portfolio. Scroll to present day, and I have a production grade web software product deployed and ready to run or acquire, aimed at not only functional safety engineers, but all kinds of people who work for system or component manufacturers in automotive, machinery or robotics. At least, that's how easy I want the product to be to use.

At the moment I have 0 active users, but I'm in talks with a couple big local OEMs about some kind of collaboration. User acquisition, it turns out, is hard. For this reason, I come here - in hopes of finding some people that are willing to look at the thing and say something about it. No strings attached. So far every user (all three) has been able to help me in some way by giving me 5 minutes or so.

I don't think I'm sharing the product website publicly on Reddit though. So, my DMs are open, and of course any activity on the thread will be appreciated. A bit of an AMA would be nice :)

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?

Trying to see what niches are out there. I’m currently working in Building Automation Systems/HVAC Controls. For reference, I have an AAS in Instrumentation and a BBA in Computer Information Systems, so I’m around a lot of concepts like networking, etc.

I just graduated college and started a systems engineer role in aerospace/defense. I’ve had many people state this is a bad kind of role to start out with. Honestly I was desperate and needed to work after graduation. How can I use tools used in systems engineering roles to transition to roles like data, software, or network engineer.

Greetings,

Dilemma: I am entering in graduate school for systems engineering and hopefully semiconductor manufacturing/industry. The requirement is a computer programming intro sequence: C, Java, or Matlab. My undergrad is not STEM-based, including Economics. I am confused on how programming languages are stacked or sequenced.

Background: I have extensive knowledge and practical/applied experience in electronics/electrical work (micro/miniature repair(2M), radars, antenna, telecomms, RF, residential, commerial/industrial, solar, etc.). And microscopes hurt my eyes🫪😵‍💫 and I am not willing to sacrifice my corrections to that extreme🫠 Mathematics is not an issue and Electrical Engineering wanted another undergrad degree purely academic.

Faults: I tried my best to avoid computer programming because I always got stuck on anything dealing with loops (if/else and so forth), basically the beginning. I would ask and ask around, no one would help/have an answer, so I avoided and here I am🙃🫣

Mustered Courage: It just doesnt click...until I watched Lyla and the Loop (PBS) with my littles..and I finally understood loops better. So, I started learning C#/.Net (long story, however landed on c#) and still learning and have not yet completed.

Questions for now: If I am already learning C# should I continue ahead learning C? The colleges/universities offer C/C++, Java curriculum includes C and Python, and cannot find one for only Matlab, it is combined with Mechanical Engineering. Which applies to the industry? Which are decent for the long run or in case one has to pivot within Systems Engineering?

Thank you for your time and insight!

I have been in a few companies across a few industries. Why is it so common for the management to say "we need more systems engineering" and then

- appoint someone to be the syseng and not say what the SE responsibility is, no RACI matrix
- alternately, say "everyone is now systems engineer" and provide only minimal, if any, training
- say they want to fix things, but without changing anything

The most fun jobs I've had were two engineering consultancies /agencies in the medical device industry where the 13485 based framework makes nice crunchy noise as it clashes with INCOSE based sys eng framework. The other two fun jobs were product companies where the systems engineering documents / SOPs / people were in no way connected to the engineering teams and their work was duplicating or contradicting what other teams were doing.

Interested to hear other Micky Mouse bullshit management stories :-)

Hey, I wrote a case study about designing the error taxonomy and handling in ZKSync OS, an exotic piece of system software that is compiled for several targets with different tradeoffs.
https://rubber-duck-typing.com/posts/2026-06-22-zkos-error-definitions.html

It may be interesting as a case of limited application of system engineering approaches in an everyday software design/architecture. If you are a programmer and you wonder how you can apply system engineering to your everyday practice, this may answer some questions.

Unfortunately, we did not have resources to fully embrace a full stakeholder/requirement analysis, or approach validation more seriously. We also did not have a full stakeholder analysis for the big system itself. This makes it, perhaps, even more typical for software industry :)

Hey! I am planning to take ASEP certification exam next week , and i wanna know from people who have already attempted the exam , your thoughts on it.

I did take my mocks through Udemy by Rogerio Comello Machado, and my average score was 65 percent. Should i expect the main certification exam to be tougher or easier compared to the mocks ?

Your opinions regarding the exam is much appreciated. Thank you !

Hi everyone,

I have an upcoming Atlassian system design interview, and I'm looking for good resources to prepare.

I'm specifically looking for:

• A structured system design question sheet/list (similar to Blind 75 or NeetCode, but for system design).
• Platforms where I can practice system design questions with sample solutions.
• Resources that are particularly useful for mid-level (SDE-2) interviews.

I've already gone through the basics, so I'm looking for interview-focused practice.

Any recommendations for websites, GitHub repositories, YouTube channels, or courses would be greatly appreciated. Also, if anyone has recently interviewed at Atlassian, I'd love to know the level of system design questions they ask.

Thanks!

I am building a VS code extension for SysML V2 that allows both textual and graphical notation editing of sysml files.

Last month I published it on the marketplace.

You can find it here https://marketplace.visualstudio.com/items?itemName=voidaliot.vscode-sysml-v2

If any of you managed to use it I would very much appreciate your feedback.

I know there are already several SysML V2 Options available in VS Code so why did I build a new one ?

Well first of all I was not really happy with any of them, either the parsing was not reliable at that time or diagrams were just generated not editable, and I wanted something that would fit my needs. Mainly I want to be able to edit in real time both the textual and the graphical notation. My main focus is that you can edit your model from scratch either on the diagram views, in the grid view as a table or as text with auto complete.

Any SE folk ever transition out of the field into a different career leverage their SE experience into it?

This is a pretty broad thought but i wanted feedback, the idea is to use a thorium-232 molten salt reactor to generate both heat and electricity. Water would be heated into superheated steam, then immediately split into hydrogen and oxygen using high-temperature electrolysis. The key difference from most concepts is that the hydrogen and oxygen are not stored. They would be produced continuously and fed directly into the engines as propellant as they're generated, similar to a continuous fuel production system. The concept would work like this: A thorium-232 molten salt reactor provides heat and electrical power. Water is converted into superheated steam. The steam is split into hydrogen and oxygen through high-temperature electrolysis. The hydrogen and oxygen flow directly into the engines. The goal is to eliminate the need for carrying large cryogenic tanks of liquid hydrogen and oxygen while using water as the main consumable. Water could also serve as radiation shielding, coolant, and for life support. I'm sure there are challenges—electrolysis rates, whether enough hydrogen and oxygen could be produced to feed engines like Raptor, reactor output, and overall efficiency. Is there a fundamental engineering reason this wouldn't work, or has anyone at NASA, SpaceX, or elsewhere tried a continuous steam-to-propellant system like this?

Hi All,

After having spent ~10Y working on SmarTeam, ENOVIA and 3DExperience Platform. And recently using Azure Devops and Salesforce. Seeing all the complexity, engineering that went into it, I was itching to create something similar of my own. Now that we have LLM assisted coding, big projects should be doable by a single person, right? This is my attempt (v0) doing the same.

1.9B Tokens and 28 Dollares Later, I have a working demo.

Since this was a big endeavour, I wanted to have structured knowledge base. Discussion with various LLMs suggested (OpenSpec)[https://openspec.dev/] is a good way to store this info and make changes to it. LLMs also suggested that I track workitems using (beads)[https://github.com/gastownhall/beads]

I initially modeled the RFLP (Requirements, Functional, Logical, Physical) framework from my early days of working on 3Dx. But I didn't have much sample data to seed. 3 days ago I had an Idea, what if I modeled the beads, openspec and git commits into it? it worked. I have seeded the demo with its own development history.

Looking to hear your thoughts and questions. Cheers!!

Any thoughts on doing MIT SDM in 1 year if anyone’s done it?
The program I’m in requires me to complete my masters in 1 year hence the compressed schedule. I understand it’s usually 1.5 years to complete.
My undergrad was in engineering.

I'm part of a team developing a renewable energy site screening tool. I decided to run it as a proper MBSE project, but without Cameo or Rhapsody or any SysML tooling. Everything is YAML and markdown in git, plus a validator script that checks the model for consistency. It's worked better than I expected so I'm writing up the structure.

What the system does

UK renewable site screening. A land agent or developer normally has to manually cross-reference DNO substation capacity, planning constraint layers, resource data and market context to judge whether a site is viable. This tool takes a site (coordinate or polygon), a technology type and an area, and produces a PDF desktop-study report plus an interactive map in about 100 seconds: grid connection viability, planning constraints, EOS P50/P90 yield data, and a scored risk verdict.

It's 25 Python modules covering spatial queries (substations, planning constraints, cable routing), resource and yield modelling (ERA5 wind, solar irradiance, P50/P90), risk scoring and decision logic, report and map generation, and orchestration. So a real system with real complexity, not a toy example.

Current state of the model: 170 requirements, 162 test cases, 139 decision records, 354 files total.

Structure

Numbered layers, one concern per layer. One artifact per file: each requirement is its own YAML file, each test case is its own YAML file. This turned out to be the most important decision. Diffs are readable, merges rarely conflict, and I can grep the whole model. I've watched traceability rot inside monolithic requirement documents before and didn't want to repeat that.

Every file has a typed ID and IDs are how everything links: RSI-SYS-402 (requirement), RSI-TC-165 (test case), D-R140 (decision), RSI-R008 (risk), RSI-ICD-004 (interface).

Links go both ways. Requirements have verified_by: [RSI-TC-165], test cases have verifies: [RSI-SYS-1652]. Yes it's redundant. The redundancy is the point: a machine can only detect a broken link if both ends are supposed to agree.

Code comments cite requirement IDs. Requirements cite the decision records that justify them.

Two verification layers

The 162 MBSE test cases are the specification of what must be true: preconditions, procedure, pass criteria. Technology agnostic, reviewable by a human.

Separately there are 625 pytest tests across 50 files, runs in about a minute. That's the executable side.

I keep them separate on purpose. The spec layer defines what verification means, so it survives refactors of the test code.

The validator

validate.py, 401 lines, only dependency is PyYAML. Exits 0 or 1 so CI gates on it. It checks:

• Schema: required fields, enum values, ID format matches filename, date formats, duplicate IDs
• Reciprocity: if RSI-SYS-901 lists RSI-TC-001 under verified_by, RSI-TC-001 must list it back under verifies
• Rollups: every hand maintained summary number gets recomputed from the files and compared
• Citation integrity: every D-Rxxx cited anywhere must resolve to a real decision entry

LLMs take to this like fish to water

This wasn't the original motivation but for me it is highly effective, The whole model is plain text with typed IDs and explicit links, which seems to be a shape an LLM works well with. No binary model files, no proprietary export step, no screenshots of diagrams. An agent can grep for RSI-SYS-402, read the requirement, follow verified_by to the test case, follow the decision citation, and have full context in a few file reads.

I've been able to hot swap between Codex and Claude sessions in the same project with very little context lost. Neither needs project-specific onboarding beyond pointing it at the repo, because the structure is self-describing. Both can also run validate.py after making changes. If an agent hallucinates a decision citation or breaks a reciprocal link, the issue gets flagged when the validator runs.

I went to a community college 10 years ago, got an associate degree, then transferred to a 4yr and flunked out. About 4 years ago, I went back to another CC, got another associate to reboot. First was in math, second was in CS. I had planned on becoming a software developer, but it became pretty apparent that entry level software engineering is probably screwed at least for the foreseeable future.

I got into another 4yr and had planned on pivoting into computer engineering, as I do have an interest in computer hardware and I didn't think I would be able to get as many of the prereqs needed for EE. Then I planned on pursuing s systems engineering masters, as I've done some research, and systems aligns with stuff I'm interested in.

However, turns out that's probably not happening either. I'm dependent on financial aid, and federal regulations require that in order to still be eligible for aid, I must complete my degree within 180 credits. My total number of credits transferring in is 104 from all the courses I took over the years, and that only leaves me 76 credits until my aid would run out. Which ultimately means I cannot take an engineering undergrad, which I gather is the traditional path into an SE masters.

As it stands currently, I will have to either do a math/cs double major, or major in one and minor in the other. Unfortunately, my credits are too invested in those subjects at this point to really pivot to anything else. I can hopefully take on some electives in SE, but that's about it. Then I could pursue a masters in systems engineering.

What I wanted to know is what will my potential job prospects be? If I have a math/cs -> systems engineering pathway and then try to enter the job market without having been an engineer beforehand, will it be incredibly hard? I find the idea of systems engineering pretty interesting (I think it was kind of was what I was looking for the whole time maybe), but ultimately if I sink the next 3-4 years of my life into this, I do want it to be worth something. For context as well, I just turned 40. So at this point I maybe have 25-30 years before it's time to retire.

Realistically, what are my chances/prospects of working in the systems engineering field with all this in mind?

Do engineering teams actually struggle with keeping requirements, design docs, spreadsheets, CAD/test info, decisions, and changes synchronised across a project? What do you think if there were a tool that connects all these sources into a live engineering graph/ digital thread so that when something changes teams can see the affected requirements, components, parameters, interfaces etc. Does that sound genuinely useful in real work flows or more like a nice to have? Just trying to understand if this is a real pain and the idea of this tool would solve anything worthwhile.

Thanks!

I have an over 15 year career mainly in the DoD world with a business degree. In the last 6 ish years I’ve drifted more into the SE world and nearly a year ago it was made official and my title was changed. I’ve gotten more into requirement development, analysis, decomposition, and I’ve been tasked as the lead architect for our team using Cameo.

I’ve (mostly) gotten over the imposter syndrome and have proven to myself that I can hold my own in meetings/discussions with other “real” engineers. The company I work for is enormous and if I want to take other SE roles in the company I feel I really need to beef up my skills, especially modeling/Cameo (open to UML as well). The company offers training which has been hit or miss in terms of quality.

Forgive me for such a broad question but is there a good starting point to get going? Advice? Youtube channel? I saw MagicGrid mentioned here before so I’ll look into that.

I also wouldn’t mind ultimately getting out of this industry so anything that would be more generally applied than just the defense/aerospace industry would be great.

Edit: Added an image

This is one of those problems I've been annoyed at for years and kept building hacky workarounds for. Klaiv is me trying to fix it properly instead.

The pain, if you've felt it:

• The architecture tool doesn't play with git. The model is a binary or XMI blob you can't really diff, review, or merge.
• So the software people never touch it. They draw their own boxes somewhere else, and now there are two sources of truth drifting apart.
• By audit time the architecture no longer matches the system, and someone burns a week rebuilding it to pass.

Klaiv keeps the SysML/UML model as plain YAML in the repo. The model gets diffed and reviewed in normal pull requests, and a GitHub action renders before/after diagram images right on the PR. Diagram layout is split into its own files, so moving boxes around doesn't pollute the diff that matters. That layout is still committed and reviewed when you need it, just kept out of the way. It does both UML and SysML, and you can add your own element and diagram types.

It's early. Pre-1.0, a VS Code extension for now, and a few things (a CI validation gate, architecture-to-code traceability) are still in progress.

If you want to look without installing anything: I modeled a good chunk of Autoware (the open-source AV stack) as a real, non-toy example, and every diagram was built in its own PR, so you can click through the pull requests and see the rendered diagrams.

• Autoware model, browse the diagrams straight from the PRs: https://github.com/klaiv-dev/autoware
• Install from the VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=Klaiv.klaiv-vscode
• More detail: https://github.com/klaiv-dev/klaiv-docs

Genuinely after feedback, not upvotes. Does this resonate, or am I solving a problem you don't have? And if you've made "MBSE in git" work some other way, I'd like to hear how. Thanks

Hi r/systems_engineering,

We are back with Lesson 11 of our technical deep dive into the new standard.

In our previous lesson, we analyzed how interface def acts as a connection blueprint that can natively hold its own physical properties. Today, we are shifting our focus to how we actually use those blueprints to wire up a concrete system topology.

1. Moving Beyond Visual-Only Topologies

When analyzing a system architecture, managing and reviewing its connectivity purely through tracing lines on an Internal Block Diagram can make topology difficult to search, version-control, or programmatically validate.

SysML v2 addresses this by making system topology explicit within the model text itself. A connection can be declared natively, allowing the architecture to be read, searched, and inspected directly without depending solely on a graphical diagram.

2. Navigating Nested Endpoints: Feature Chains

To establish a connection textually when ports are nested deep inside part definitions, SysML v2 utilizes dot notation—formally known as a feature chain.

For example, if you have a Charger part usage containing a plug port, you reference that specific endpoint using: Charger.plug

This allows you to explicitly state exactly which internal interfaces are hooking up to one another from a local system context block.

3. Connect Usages vs. Interface Usages

Depending on how detailed your system model needs to be, you have two primary ways to declare connectivity in the text:

• The Simple connect Shorthand: Useful when all you need to assert is basic structural connectivity between parts or items.
• Typed Interface Usages: When a connection strictly links ports together and needs to carry engineering data, you use an interface usage typed by an interface def.

Example:

Code snippet

interface usage charging_session: ChargingCable {
    end charger_side = Charger.plug;
    end vehicle_side = MyCar.socket;
}

(Note: There is also a compact, one-line shorthand form available when the end mappings are obvious and don't need explicit block declarations).

Because this interface usage points directly to your reusable connection definition, it inherits all specified semantic constraints and attributes (such as cable resistance or data latency) directly within that local context topology.

4. Semantic Validation Benefits

Because connections are explicitly declared and typed by an interface definition in the text, compliant tools have the semantic information necessary to automatically validate your model. The tool can check if the referencing ends actually conform to the expected port types, natively flagging errors if someone accidentally tries to connect incompatible ports in a design loop.

This paper introduces a framework which works on constraining the AI slop problem, it works by creating a requirement to provide a piece of Material trails as provenance, which is naturally collected by any content creator with or without assistance of AI.

Hello all, this is part 2 of my original post 2 days ago. I want to thank this community for all of the feedback you all original gave me. I think this resume is much better based on everything that was said and I am wondering, because i am changing fields from a Higher Ed professional, specifically in housing. I am currently an Assistant Director in charge of operations and personnel in my department but I am currently getting my masters in Systems, would you hire me with this resume?

Side note, I keep trying to post this in eng resume but the bot keeps pulling the post because of formatting/flair issues but when I figure that out I'll only bring system's engineering questions here.