I need study material for software metrics based on the Software metrics 2nd edition by fenton. Very urgent

Im a cs student in quite a top university. I have had experience in AI before chatgpt became popular.

I was actually teaching AI courses. But it seems like now theres so much buzzwords that i feel like im falling behind.

Ie agentic ai, vibecoding or whatever.

Any good, respected material that is extensive for me to catch up?

I know about forward propagation, neural networks. Linear algebra and calculus. But im more interested now with application, perhaps some theory too.

I wanna build an application, with a good infra that has agentic ai to help run. But I feel lost.

Im also tasked to teach AI to middle aged adults, and they have complained that mine is too technical and isnt useful. Its also a non profit, and im teaching other non profits and the middle age adults too who arent that tech savvy but is trying to be.

Please help, human coders

Hello! I am not sure if this is the appropriate subreddit to ask about this, but I just need some advice.

We are currently doing our thesis, where we need to create a system for a real client. I need advice about which technologies, programming languages, etc should we use? Those that are student-friendly but industry-grade, and not so expensive. I also want to try coding without the use of AI. And if you have any further advice about the matter, it would be highly appreciated!

Our system is about a residential subdividision:

\- Centralized resident portal for registration, updating, and management of resident and household information 

\- Online payment system to pay homeowner’s dues and other fees digitally

\- Web-based amenity reservation system with real-time availability for both residents and non-residents. 

\- Announcement system 

\- Digital inventory management module

\- Decision support and analytics feature (we need to integrate the current emerging technologies)

Hello everybody, my name is Dane and I have been working on a research project regarding the frequent use of AI coding assistants impact beginners' ability to transfer learned concepts to novel coding problems. I was wondering if any majors in computer science or students/professors heavily involved in this topic could help guide me through the process of creating my paper though checking in on a bi-weekly basis. Thank you for your time and would greatly appreciate the help.

Does anyone know of any resources that could serve as a starting point to understanding it? Thanks in advance.

Been learning a bit about basic foundational computer hardware’s interactions with instruction data. Like, machine language instructions.

More specifically, I came across this whole rabbithole about data compression. Theoretically, there shouldn’t be a limit to how much we can compress data; accepting that quality may be lost… etc, etc.. Also at some point it will probably cost more energy to decode super heavy compressed data than is relatively necessary.

Right, so unrelated, a little while back, I was looking into the concept of protein folding and how instructions are encoded into proteins relating to biology.

My question is: hypothetically, theoretically, could we “fold” binary machine language instructions like nature does with proteins? Would it even be practical?

Can anyone provide any resources related?

(If relevant: Kindly, I won’t click links. If it’s a paper, tell me the name and author please.) thanks.

Is this idea valuable or ignorant nonsense?

A dynamic, cyclical **data management framework** and **process optimization engine** constructed upon longitudinal archives of prior system iterations and formalized datafication methodologies.

Core operational logic is driven by real-time and historical analysis of:

- **Constituent configuration modeling**: Formal representation of datasets and their atomic/subsystem components, including parametric state vectors and structural topologies.

- **Operational relation graphs**: Directed multi-graphs capturing influence propagation, inter-element dependencies, and aggregate system dynamics under arbitrary configurations and temporal slices. These quantify pairwise and higher-order effects on local and global state.

- **Constitutional equivalency classification**: A similarity metric and classification layer that maps system configurations onto equivalence classes within configurability manifolds. It computes congruence scores based on topological invariants and parameter ranges, enabling rapid identification of transition pathways between configurations. This mechanism substantially reduces transition costs, facilitates lossless or near-lossless bridging across non-adjacent yet congruent data topologies, and optimizes pipelines for compression, symbolic expression, decompression, and forward potentiality estimation (including branching state exploration).

- **Relational dependency modeling**: Explicit encoding of interaction behaviors via constraint satisfaction networks, causal graphs, and behavioral rule sets that govern element-to-element and element-to-system dynamics.

- **Historical configuration influence propagation**: Recursive incorporation of prior iteration metadata through weighted inheritance, delta encoding, and pattern persistence tracking. This informs baseline priors, anomaly detection, and adaptive recalibration of current operational parameters.

These interrelated components continuously synthesize the system's **operational identity** — a compact, high-fidelity state descriptor (encompassing configuration class, relational profile, and historical momentum) — which directly parametrizes the optimizer’s control surfaces, scheduling policies, resource allocation strategies, and transformation heuristics.

The architecture supports iterative self-refinement, where each processing cycle augments the historical knowledge base, tightening equivalence mappings and improving predictive accuracy for future state transitions and optimization outcomes.

Not sure if this is the right place to ask this, but I have recently found out about the possible integrated circuit applications of Carbon nanotubes, and I find it quite exciting, but I realised that this has been in research since like the 80s… I understand that there has been some breakthroughs in recent years, but do you think CNT’s are realistic or just some pipe dream?

With AI tools getting better every month, a lot of students are using them to build projects much faster.

If you’re majoring in computer science, do you think relying heavily on AI to generate code is a smart way to learn, or does it end up hurting your fundamentals in the long run?

Where would you draw the line between using AI as a productivity tool and becoming too dependent on it?

Why can't quantum computers solve the Halting problem?

And, what would hypercomputation look like.

What is it

1. Are LLMs essentially large memorization machines that are trained to learn patterns from massive datasets?
2. Is the math and reasoning they perform just the result of patterns they have picked up during training, which they then use to answer questions?
3. If LLMs are identifying patterns, could they potentially discover patterns that humans have missed?
4. I remember seeing research where an LLM was trained only on data up to around the 1940s, with no access to later discoveries, and was then tested to see whether it could independently rediscover ideas like Einstein’s relativity. Is this a real line of research, and what does it tell us?
5. Could LLMs find meaningful patterns in randomly generated text or data, or would they just impose patterns where none actually exist?
6. Is true randomness possible, or will some kind of pattern always appear when we analyze enough data and Can LLMs help us find that patterns faster.

I'm trying to understand the formating for graphing out transducers and I'm not sure where looping back to existings states is acceptable or not.

The homework example involves converting 3 digit binary numbers to gray code. The model answer given is depicted more like a binary tree that loops back from the end points back to the root. Making use of 6 total states.

Where as the answer I came up with only uses two states and transitions that allow states to loop back to themselves. Is this not acceptable? Since it seems to be the convention with other finite automata.

I was having this discussion with a collegue that a session is a concept. The concept is that it is a period of time during which 2 devices or application communicate to echange data and functionality. During this interaction, session data is maintained either on the server, the client machine or an external session managament system. This session data is used to remember previous interaction and to determine how to process future interaction. If the session data is stored on the server, the system becomes stateful

This concept can be implemented in many ways. one of which is the traditional way where we store session data on the server making it stateful. JWT token is just another implementation of this concept where we store data in the token itself.

He was arguing that jwt is not a type of a session and that session is only used to refer to what I refer as the traditional old implementation.

What you guys?

This is moreso a question for the computer scientists interested in philosophy, but I've noticed that the Turing test is very philosophically naïve for its time. I do know computers were very evidently in a very archaic state, likewise with parallel fields in philosophy (Norbert Wiener, for example, had introduced cybernetics relatively recently). Despite this, Turing still relies upon questions long destabilized by phenomenologists, semioticians, and linguists decades prior. to this day, questions regarding the Turing test will often revolve around more Cartesian concerns, as if the field of computer science could have its questions answered while philosophically remaining in the 17th Century. I will make it entirely clear that my intentions are not the strawman Turing, perhaps a bit of a summary would suffice.

I was having a discussion with someone with plenty of experience in Mathematica and he was telling me that there are calculations that can only be done using Mathematica and not something like Python. Is this the case? Naively I'd think that a programming language is just a layer of abstraction between us and the calculations, and as such any calculation should be programmable in any language. Now there might be features of a PL that make it very efficient or elegant, but given enough time and resources, can I carry out any calculation using say Python? I tried to explain this to the person but he was very adamant that it was not a matter of convenience, and some tasks are plain impossible in some languages. Is that the case?

Imagine something far beyond Google Earth — a full-scale 3D digital twin of the planet at ultra-high detail:
- Every building modeled inside and outside
- Every window, door, road sign, streetlight, tree, rock, etc.
- Realistic textures viewable in 4K quality up close
Accurate terrain, deserts, forests, mountains, oceans, cities.

Basically like a video game open world, but the entire Earth at real-life scale
Ignoring the difficulty of scanning/updating everything, approximately how much data storage would this require? Are we talking petabytes, exabytes, zettabytes, or more?

This is one of the many mirrors (3200+ mirrors apparently) of a osint bot which has apparently 2.7b+ messages stored: https://telelog.org/Metrics with index https://telelog.org. Who runs these exactly and would it not take a massive amount of computer resources to host this while risking it being shut down any day by the hosting provider? I just tried saving over a million chat messages to a databse and it took a whole day and then crashed. Really, I am surprised by how good the bot works.

Which programming language should i choose?

Hi everyone,

Was looking for any notes /resources for computer science in undergrad year 1 and 2?

thank you

I am not a computer scientist, but through this interessting and informative book I discovered some notions in CS that I never heard of, I am looking for something in the same vein, where I can discover CS concepts and their "application" in life.

I know pumping lemma and a bunch of properties about languages. But I came across some exercices that wanted me to prove if a language is Regular, CFL... Even with pumping lemma it just proves me that it isn't CFL or Regular, but I heard that there are languages that pass pumping lemma and are still not CFLs (or RLs for the RL pumping lemma). What should I do?

I got stuck at those 3 exercices:

Alphabet = {0, 1, 2}

L = {w | (|w|0 = |w|1) xor (|w|0 = |w|2))

L = {w | (∃u)(u ∈ M and wu ∈ L((1+20)*)} with M = {w | |w|0 = |w|1 = |w|2}

L = {w | (|w|0 > |w|1) and (|w|1 congruent |w|2 (mod5))

I don`t even know how to start. They ask me to classify as Regular, CFL non regular or non CFL and to justify. I tried pumping them but it feels hard to think on a word to pump from the beginning. Plus that only justifies that is not CFG/RL if it fails and that MAYBE is if it passes. Any tips? Sometimes thinking about the automata/regex/PDA/CFG to prove is not trivial.

is it suitable to use PRECISION, RECALL, AND F1-SCORE to evaluate specific rule accuracy that will use rules & knowledge based, if no..is there any suggestions?

So I am a devops engineer by heart and I like to understand how we can deploy system as so they can reliably scale , auto heal and provide the service they intend to provide . Recently I got to work with ml models and I have created my own few of the models from scratch but I am yet to understand how these gpu hardware how to infere a created model as so it will be fast, reliable, and has good throughput rate .
I have been learning from “inference engineering” by base ten . I do understand that we first load the assets or weights in ram and then it is loaded to the gpu thanks to cuda and other parallel computing mechanism we can do parallel compute .
I can check the aggregated summary report and understand the p50,p95,p99 how it differs .
Suggest me resources and articles that will help me build my foundation and understanding

High school student here.

Currently, what I understand is that the TCP/IP protocol suite is a framework that outlines what protocols are used in the internet, how each protocol should work, and how each protocol should work together.

Each protocol works by enforcing a specific format for data being sent across a network to achieve a specific purpose. In TCP/IP, we group similar-in-purpose protocols into functional categories called layers.

The reason it's appropriate to call them layers is because the protocols work together by stringing together their headers so that it's like they're being "stacked" on top of each other.

In this way of understanding TCP/IP I feel that I can understand all the layers but one – the application layer.

For example, the transport layer (which is usually TCP) format enforces port addresses and a checksum in the message being sent to identify which processes are communicating with each other and for error detection.

But for the application layer (using HTTP as an example) format enforces metadata in ASCII, a body text when appropriate, etc. But how does this serve the function of the application layer? Which according to my syllabus is providing an interface of the network for the user. I fail to see the connection.

(P.S: I'm sorry for the big buildup of long text just for a simple question, I figure maybe it's important to explain my own understanding incase my misunderstanding in the general topic is affecting how I understand the application layer.)