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Depreciation Calculator - Insurance Claims Tools & Databases - Claims Pages
depreciation calculator for different product categories/commodities (tbh I would prefer just a table of rates)
tools  calculator  personal-finance  money  increase-decrease  flux-stasis  cost-benefit  economics  time  correlation  manifolds  data  database  objektbuch  quality 
6 weeks ago by nhaliday
The Definitive Guide To Website Authentication | Hacker News
hn  commentary  q-n-a  stackex  programming  identification-equivalence  security  web  client-server  crypto  checklists  best-practices  objektbuch  api  multi  cheatsheet  chart  system-design  nitty-gritty  yak-shaving  comparison  explanation  summary  jargon  state  networking  protocol-metadata  time 
november 2019 by nhaliday
exponential function - Feynman's Trick for Approximating $e^x$ - Mathematics Stack Exchange
1. e^2.3 ~ 10
2. e^.7 ~ 2
3. e^x ~ 1+x
e = 2.71828...

errors (absolute, relative):
1. +0.0258, 0.26%
2. -0.0138, -0.68%
3. 1 + x approximates e^x on [-.3, .3] with absolute error < .05, and relative error < 5.6% (3.7% for [0, .3]).
nibble  q-n-a  overflow  math  feynman  giants  mental-math  calculation  multiplicative  AMT  identity  objektbuch  explanation  howto  estimate  street-fighting  stories  approximation  data  trivia  nitty-gritty 
october 2019 by nhaliday
Japanese sound symbolism - Wikipedia
Japanese has a large inventory of sound symbolic or mimetic words, known in linguistics as ideophones.[1][2] Sound symbolic words are found in written as well as spoken Japanese.[3] Known popularly as onomatopoeia, these words are not just imitative of sounds but cover a much wider range of meanings;[1] indeed, many sound-symbolic words in Japanese are for things that don't make any noise originally, most clearly demonstrated by shiinto (しいんと), meaning "silently".
language  foreign-lang  trivia  wiki  reference  audio  hmm  alien-character  culture  list  objektbuch  japan  asia  writing 
october 2019 by nhaliday
Software Testing Anti-patterns | Hacker News
I haven't read this but both the article and commentary/discussion look interesting from a glance

hmm: https://news.ycombinator.com/item?id=16896390
In small companies where there is no time to "waste" on tests, my view is that 80% of the problems can be caught with 20% of the work by writing integration tests that cover large areas of the application. Writing unit tests would be ideal, but time-consuming. For a web project, that would involve testing all pages for HTTP 200 (< 1 hour bash script that will catch most major bugs), automatically testing most interfaces to see if filling data and clicking "save" works. Of course, for very important/dangerous/complex algorithms in the code, unit tests are useful, but generally, that represents a very low fraction of a web application's code.
hn  commentary  techtariat  discussion  programming  engineering  methodology  best-practices  checklists  thinking  correctness  api  interface-compatibility  jargon  list  metabuch  objektbuch  workflow  documentation  debugging  span-cover  checking  metrics  abstraction  within-without  characterization  error  move-fast-(and-break-things)  minimum-viable  efficiency  multi  poast  pareto  coarse-fine 
october 2019 by nhaliday
Learn to speak vim — verbs, nouns, and modifiers! - Yan Pritzker
learn some verbs: v (visual), c (change), d (delete), y (yank/copy). these are the most important. there are others
learn some modifiers: i (inside), a (around), t (till..finds a character), f (find..like till except including the char), / (search..find a string/regex)
learn some text objects: w (word), s (sentence) p (paragraph) b (block/parentheses), t (tag, works for html/xml) there are others
techtariat  howto  objektbuch  top-n  list  editors  composition-decomposition  DSL  lexical  atoms  yak-shaving  language 
august 2019 by nhaliday
[Tutorial] A way to Practice Competitive Programming : From Rating 1000 to 2400+ - Codeforces
this guy really didn't take that long to reach red..., as of today he's done 20 contests in 2y to my 44 contests in 7y (w/ a long break)...>_>

tho he has 3 times as many submissions as me. maybe he does a lot of virtual rounds?

some snippets from the PDF guide linked:
1400-1900:
To be rating 1900, skills as follows are needed:
- You know and can use major algorithms like these:
Brute force DP DFS BFS Dijkstra
Binary Indexed Tree nCr, nPr Mod inverse Bitmasks Binary Search
- You can code faster (For example, 5 minutes for R1100 problems, 10 minutes for
R1400 problems)

If you are not good at fast-coding and fast-debugging, you should solve AtCoder problems. Actually, and statistically, many Japanese are good at fast-coding relatively while not so good at solving difficult problems. I think that’s because of AtCoder.

I recommend to solve problem C and D in AtCoder Beginner Contest. On average, if you can solve problem C of AtCoder Beginner Contest within 10 minutes and problem D within 20 minutes, you are Div1 in FastCodingForces :)

...

Interestingly, typical problems are concentrated in Div2-only round problems. If you are not good at Div2-only round, it is likely that you are not good at using typical algorithms, especially 10 algorithms that are written above.

If you can use some typical problem but not good at solving more than R1500 in Codeforces, you should begin TopCoder. This type of practice is effective for people who are good at Div.2 only round but not good at Div.1+Div.2 combined or Div.1+Div.2 separated round.

Sometimes, especially in Div1+Div2 round, some problems need mathematical concepts or thinking. Since there are a lot of problems which uses them (and also light-implementation!) in TopCoder, you should solve TopCoder problems.

I recommend to solve Div1Easy of recent 100 SRMs. But some problems are really difficult, (e.g. even red-ranked coder could not solve) so before you solve, you should check how many percent of people did solve this problem. You can use https://competitiveprogramming.info/ to know some informations.

1900-2200:
To be rating 2200, skills as follows are needed:
- You know and can use 10 algorithms which I stated in pp.11 and segment trees
(including lazy propagations)
- You can solve problems very fast: For example, 5 mins for R1100, 10 mins for
R1500, 15 mins for R1800, 40 mins for R2000.
- You have decent skills for mathematical-thinking or considering problems
- Strong mental which can think about the solution more than 1 hours, and don’t give up even if you are below average in Div1 in the middle of the contest

This is only my way to practice, but I did many virtual contests when I was rating 2000. In this page, virtual contest does not mean “Virtual Participation” in Codeforces. It means choosing 4 or 5 problems which the difficulty is near your rating (For example, if you are rating 2000, choose R2000 problems in Codeforces) and solve them within 2 hours. You can use https://vjudge.net/. In this website, you can make virtual contests from problems on many online judges. (e.g. AtCoder, Codeforces, Hackerrank, Codechef, POJ, ...)

If you cannot solve problem within the virtual contests and could not be able to find the solution during the contest, you should read editorial. Google it. (e.g. If you want to know editorial of Codeforces Round #556 (Div. 1), search “Codeforces Round #556 editorial” in google) There is one more important thing to gain rating in Codeforces. To solve problem fast, you should equip some coding library (or template code). For example, I think that equipping segment tree libraries, lazy segment tree libraries, modint library, FFT library, geometry library, etc. is very effective.

2200 to 2400:
Rating 2200 and 2400 is actually very different ...

To be rating 2400, skills as follows are needed:
- You should have skills that stated in previous section (rating 2200)
- You should solve difficult problems which are only solved by less than 100 people in Div1 contests

...

At first, there are a lot of educational problems in AtCoder. I recommend you should solve problem E and F (especially 700-900 points problem in AtCoder) of AtCoder Regular Contest, especially ARC058-ARC090. Though old AtCoder Regular Contests are balanced for “considering” and “typical”, but sadly, AtCoder Grand Contest and recent AtCoder Regular Contest problems are actually too biased for considering I think, so I don’t recommend if your goal is gain rating in Codeforces. (Though if you want to gain rating more than 2600, you should solve problems from AtCoder Grand Contest)

For me, actually, after solving AtCoder Regular Contests, my average performance in CF virtual contest increased from 2100 to 2300 (I could not reach 2400 because start was early)

If you cannot solve problems, I recommend to give up and read editorial as follows:
Point value 600 700 800 900 1000-
CF rating R2000 R2200 R2400 R2600 R2800
Time to editorial 40 min 50 min 60 min 70 min 80 min

If you solve AtCoder educational problems, your skills of competitive programming will be increased. But there is one more problem. Without practical skills, you rating won’t increase. So, you should do 50+ virtual participations (especially Div.1) in Codeforces. In virtual participation, you can learn how to compete as a purple/orange-ranked coder (e.g. strategy) and how to use skills in Codeforces contests that you learned in AtCoder. I strongly recommend to read editorial of all problems except too difficult one (e.g. Less than 30 people solved in contest) after the virtual contest. I also recommend to write reflections about strategy, learns and improvements after reading editorial on notebooks after the contests/virtual.

In addition, about once a week, I recommend you to make time to think about much difficult problem (e.g. R2800 in Codeforces) for couple of hours. If you could not reach the solution after thinking couple of hours, I recommend you to read editorial because you can learn a lot. Solving high-level problems may give you chance to gain over 100 rating in a single contest, but also can give you chance to solve easier problems faster.
oly  oly-programming  problem-solving  learning  practice  accretion  strategy  hmm  pdf  guide  reflection  advice  wire-guided  marginal  stylized-facts  speed  time  cost-benefit  tools  multi  sleuthin  review  comparison  puzzles  contest  aggregator  recommendations  objektbuch  time-use  growth  studying  🖥  👳  yoga 
august 2019 by nhaliday
How to make a fast command line tool in Python
An overview of why Python programs tend to be slow to start running, and some techniques Bazaar uses to start quickly, such as lazy imports.
techtariat  presentation  howto  objektbuch  tutorial  python  programming  performance  tricks  time  latency-throughput  yak-shaving  build-packaging 
august 2019 by nhaliday
The Compositional Nature of Vim - Ismail Badawi
https://medium.com/@mkozlows/why-atom-cant-replace-vim-433852f4b4d1
1976 was a good year for text editors. At MIT, Richard Stallman and Guy Steele wrote the first version of Emacs. And over at Berkeley, Bill Joy wrote vi (though it wouldn’t be called that for a few years yet).
It’s reductionist to say that these two editors were each built around one big idea, but what the hell, let’s be reductionist. Because what stands out today, looking at modern editors like Sublime Text and Atom, is how Emacs’ big idea has been thoroughly learned — and how vi’s big idea hasn’t.

Emacs and Extensibility
Vi and Composability
techtariat  editors  yak-shaving  productivity  workflow  composition-decomposition  metabuch  howto  syntax  lexical  objektbuch  degrees-of-freedom  flexibility  DSL  multi  integration-extension  org:med  atoms 
august 2019 by nhaliday
c++ - mmap() vs. reading blocks - Stack Overflow
The discussion of mmap/read reminds me of two other performance discussions:

Some Java programmers were shocked to discover that nonblocking I/O is often slower than blocking I/O, which made perfect sense if you know that nonblocking I/O requires making more syscalls.

Some other network programmers were shocked to learn that epoll is often slower than poll, which makes perfect sense if you know that managing epoll requires making more syscalls.

Conclusion: Use memory maps if you access data randomly, keep it around for a long time, or if you know you can share it with other processes (MAP_SHARED isn't very interesting if there is no actual sharing). Read files normally if you access data sequentially or discard it after reading. And if either method makes your program less complex, do that. For many real world cases there's no sure way to show one is faster without testing your actual application and NOT a benchmark.
q-n-a  stackex  programming  systems  performance  tradeoffs  objektbuch  stylized-facts  input-output  caching  computer-memory  sequential  applicability-prereqs  local-global 
july 2019 by nhaliday
Errors in Math Functions (The GNU C Library)
https://stackoverflow.com/questions/22259537/guaranteed-precision-of-sqrt-function-in-c-c
For C99, there are no specific requirements. But most implementations try to support Annex F: IEC 60559 floating-point arithmetic as good as possible. It says:

An implementation that defines __STDC_IEC_559__ shall conform to the specifications in this annex.

And:

The sqrt functions in <math.h> provide the IEC 60559 square root operation.

IEC 60559 (equivalent to IEEE 754) says about basic operations like sqrt:

Except for binary <-> decimal conversion, each of the operations shall be performed as if it first produced an intermediate result correct to infinite precision and with unbounded range, and then coerced this intermediate result to fit in the destination's format.

The final step consists of rounding according to several rounding modes but the result must always be the closest representable value in the target precision.

[ed.: The list of other such correctly rounded functions is included in the IEEE-754 standard (which I've put w/ the C1x and C++2x standard drafts) under section 9.2, and it mainly consists of stuff that can be expressed in terms of exponentials (exp, log, trig functions, powers) along w/ sqrt/hypot functions.

Fun fact: this question was asked by Yeputons who has a codeforces profile.]
https://stackoverflow.com/questions/20945815/math-precision-requirements-of-c-and-c-standard
oss  libraries  systems  c(pp)  numerics  documentation  objektbuch  list  linux  unix  multi  q-n-a  stackex  programming  nitty-gritty  sci-comp  accuracy  types  approximation  IEEE  protocol-metadata  gnu 
july 2019 by nhaliday
Laurence Tratt: What Challenges and Trade-Offs do Optimising Compilers Face?
Summary
It's important to be realistic: most people don't care about program performance most of the time. Modern computers are so fast that most programs run fast enough even with very slow language implementations. In that sense, I agree with Daniel's premise: optimising compilers are often unimportant. But “often” is often unsatisfying, as it is here. Users find themselves transitioning from not caring at all about performance to suddenly really caring, often in the space of a single day.

This, to me, is where optimising compilers come into their own: they mean that even fewer people need care about program performance. And I don't mean that they get us from, say, 98 to 99 people out of 100 not needing to care: it's probably more like going from 80 to 99 people out of 100 not needing to care. This is, I suspect, more significant than it seems: it means that many people can go through an entire career without worrying about performance. Martin Berger reminded me of A N Whitehead’s wonderful line that “civilization advances by extending the number of important operations which we can perform without thinking about them” and this seems a classic example of that at work. Even better, optimising compilers are widely tested and thus generally much more reliable than the equivalent optimisations performed manually.

But I think that those of us who work on optimising compilers need to be honest with ourselves, and with users, about what performance improvement one can expect to see on a typical program. We have a tendency to pick the maximum possible improvement and talk about it as if it's the mean, when there's often a huge difference between the two. There are many good reasons for that gap, and I hope in this blog post I've at least made you think about some of the challenges and trade-offs that optimising compilers are subject to.

[1]
Most readers will be familiar with Knuth’s quip that “premature optimisation is the root of all evil.” However, I doubt that any of us have any real idea what proportion of time is spent in the average part of the average program. In such cases, I tend to assume that Pareto’s principle won't be far too wrong (i.e. that 80% of execution time is spent in 20% of code). In 1971 a study by Knuth and others of Fortran programs, found that 50% of execution time was spent in 4% of code. I don't know of modern equivalents of this study, and for them to be truly useful, they'd have to be rather big. If anyone knows of something along these lines, please let me know!
techtariat  programming  compilers  performance  tradeoffs  cost-benefit  engineering  yak-shaving  pareto  plt  c(pp)  rust  golang  trivia  data  objektbuch  street-fighting  estimate  distribution  pro-rata 
july 2019 by nhaliday
Computer latency: 1977-2017
If we look at overall results, the fastest machines are ancient. Newer machines are all over the place. Fancy gaming rigs with unusually high refresh-rate displays are almost competitive with machines from the late 70s and early 80s, but “normal” modern computers can’t compete with thirty to forty year old machines.

...

If we exclude the game boy color, which is a different class of device than the rest, all of the quickest devices are Apple phones or tablets. The next quickest device is the blackberry q10. Although we don’t have enough data to really tell why the blackberry q10 is unusually quick for a non-Apple device, one plausible guess is that it’s helped by having actual buttons, which are easier to implement with low latency than a touchscreen. The other two devices with actual buttons are the gameboy color and the kindle 4.

After that iphones and non-kindle button devices, we have a variety of Android devices of various ages. At the bottom, we have the ancient palm pilot 1000 followed by the kindles. The palm is hamstrung by a touchscreen and display created in an era with much slower touchscreen technology and the kindles use e-ink displays, which are much slower than the displays used on modern phones, so it’s not surprising to see those devices at the bottom.

...

Almost every computer and mobile device that people buy today is slower than common models of computers from the 70s and 80s. Low-latency gaming desktops and the ipad pro can get into the same range as quick machines from thirty to forty years ago, but most off-the-shelf devices aren’t even close.

If we had to pick one root cause of latency bloat, we might say that it’s because of “complexity”. Of course, we all know that complexity is bad. If you’ve been to a non-academic non-enterprise tech conference in the past decade, there’s a good chance that there was at least one talk on how complexity is the root of all evil and we should aspire to reduce complexity.

Unfortunately, it's a lot harder to remove complexity than to give a talk saying that we should remove complexity. A lot of the complexity buys us something, either directly or indirectly. When we looked at the input of a fancy modern keyboard vs. the apple 2 keyboard, we saw that using a relatively powerful and expensive general purpose processor to handle keyboard inputs can be slower than dedicated logic for the keyboard, which would both be simpler and cheaper. However, using the processor gives people the ability to easily customize the keyboard, and also pushes the problem of “programming” the keyboard from hardware into software, which reduces the cost of making the keyboard. The more expensive chip increases the manufacturing cost, but considering how much of the cost of these small-batch artisanal keyboards is the design cost, it seems like a net win to trade manufacturing cost for ease of programming.

...

If you want a reference to compare the kindle against, a moderately quick page turn in a physical book appears to be about 200 ms.

https://twitter.com/gravislizard/status/927593460642615296
almost everything on computers is perceptually slower than it was in 1983
https://archive.is/G3D5K
https://archive.is/vhDTL
https://archive.is/a3321
https://archive.is/imG7S
techtariat  dan-luu  performance  time  hardware  consumerism  objektbuch  data  history  reflection  critique  software  roots  tainter  engineering  nitty-gritty  ui  ux  hci  ios  mobile  apple  amazon  sequential  trends  increase-decrease  measure  analysis  measurement  os  systems  IEEE  intricacy  desktop  benchmarks  rant  carmack  system-design  degrees-of-freedom  keyboard  terminal  editors  links  input-output  networking  world  s:**  multi  twitter  social  discussion  tech  programming  web  internet  speed  backup  worrydream  interface  metal-to-virtual  latency-throughput  workflow  form-design  interface-compatibility 
july 2019 by nhaliday
How to work with GIT/SVN — good practices - Jakub Kułak - Medium
best part of this is the links to other guides
Commit Often, Perfect Later, Publish Once: https://sethrobertson.github.io/GitBestPractices/

My Favourite Git Commit: https://news.ycombinator.com/item?id=21289827
I use the following convention to start the subject of commit(posted by someone in a similar HN thread):
...
org:med  techtariat  tutorial  faq  guide  howto  workflow  devtools  best-practices  vcs  git  engineering  programming  multi  reference  org:junk  writing  technical-writing  hn  commentary  jargon  list  objektbuch  examples  analysis 
june 2019 by nhaliday
c++ - Which is faster: Stack allocation or Heap allocation - Stack Overflow
On my machine, using g++ 3.4.4 on Windows, I get "0 clock ticks" for both stack and heap allocation for anything less than 100000 allocations, and even then I get "0 clock ticks" for stack allocation and "15 clock ticks" for heap allocation. When I measure 10,000,000 allocations, stack allocation takes 31 clock ticks and heap allocation takes 1562 clock ticks.

so maybe around 100x difference? what does that work out to in terms of total workload?

hmm:
http://vlsiarch.eecs.harvard.edu/wp-content/uploads/2017/02/asplos17mallacc.pdf
Recent work shows that dynamic memory allocation consumes nearly 7% of all cycles in Google datacenters.

That's not too bad actually. Seems like I shouldn't worry about shifting from heap to stack/globals unless profiling says it's important, particularly for non-oly stuff.

edit: Actually, factor x100 for 7% is pretty high, could be increase constant factor by almost an order of magnitude.

edit: Well actually that's not the right math. 93% + 7%*.01 is not much smaller than 100%
q-n-a  stackex  programming  c(pp)  systems  memory-management  performance  intricacy  comparison  benchmarks  data  objektbuch  empirical  google  papers  nibble  time  measure  pro-rata  distribution  multi  pdf  oly-programming  computer-memory 
june 2019 by nhaliday
PythonSpeed/PerformanceTips - Python Wiki
some are obsolete, but I think, eg, the tip about using local vars over globals is still applicable
wiki  reference  cheatsheet  objektbuch  list  programming  python  performance  pls  local-global 
june 2019 by nhaliday
C++ Core Guidelines
This document is a set of guidelines for using C++ well. The aim of this document is to help people to use modern C++ effectively. By “modern C++” we mean effective use of the ISO C++ standard (currently C++17, but almost all of our recommendations also apply to C++14 and C++11). In other words, what would you like your code to look like in 5 years’ time, given that you can start now? In 10 years’ time?

https://isocpp.github.io/CppCoreGuidelines/
“Within C++ is a smaller, simpler, safer language struggling to get out.” – Bjarne Stroustrup

...

The guidelines are focused on relatively higher-level issues, such as interfaces, resource management, memory management, and concurrency. Such rules affect application architecture and library design. Following the rules will lead to code that is statically type safe, has no resource leaks, and catches many more programming logic errors than is common in code today. And it will run fast - you can afford to do things right.

We are less concerned with low-level issues, such as naming conventions and indentation style. However, no topic that can help a programmer is out of bounds.

Our initial set of rules emphasize safety (of various forms) and simplicity. They may very well be too strict. We expect to have to introduce more exceptions to better accommodate real-world needs. We also need more rules.

...

The rules are designed to be supported by an analysis tool. Violations of rules will be flagged with references (or links) to the relevant rule. We do not expect you to memorize all the rules before trying to write code.

contrary:
https://aras-p.info/blog/2018/12/28/Modern-C-Lamentations/
This will be a long wall of text, and kinda random! My main points are:
1. C++ compile times are important,
2. Non-optimized build performance is important,
3. Cognitive load is important. I don’t expand much on this here, but if a programming language or a library makes me feel stupid, then I’m less likely to use it or like it. C++ does that a lot :)
programming  engineering  pls  best-practices  systems  c(pp)  guide  metabuch  objektbuch  reference  cheatsheet  elegance  frontier  libraries  intricacy  advanced  advice  recommendations  big-picture  novelty  lens  philosophy  state  error  types  concurrency  memory-management  performance  abstraction  plt  compilers  expert-experience  multi  checking  devtools  flux-stasis  safety  system-design  techtariat  time  measure  dotnet  comparison  examples  build-packaging  thinking  worse-is-better/the-right-thing  cost-benefit  tradeoffs  essay  commentary  oop  correctness  computer-memory  error-handling  resources-effects  latency-throughput 
june 2019 by nhaliday
Regex cheatsheet
Many programs use regular expression to find & replace text. However, they tend to come with their own different flavor.

You can probably expect most modern software and programming languages to be using some variation of the Perl flavor, "PCRE"; however command-line tools (grep, less, ...) will often use the POSIX flavor (sometimes with an extended variant, e.g. egrep or sed -r). ViM also comes with its own syntax (a superset of what Vi accepts).

This cheatsheet lists the respective syntax of each flavor, and the software that uses it.

accidental complexity galore
techtariat  reference  cheatsheet  documentation  howto  yak-shaving  editors  strings  syntax  examples  crosstab  objektbuch  python  comparison  gotchas  tip-of-tongue  automata-languages  pls  trivia  properties  libraries  nitty-gritty  intricacy  degrees-of-freedom  DSL  programming 
june 2019 by nhaliday
Basic Error Rates
This page describes human error rates in a variety of contexts.

Most of the error rates are for mechanical errors. A good general figure for mechanical error rates appears to be about 0.5%.

Of course the denominator differs across studies. However only fairly simple actions are used in the denominator.

The Klemmer and Snyder study shows that much lower error rates are possible--in this case for people whose job consisted almost entirely of data entry.

The error rate for more complex logic errors is about 5%, based primarily on data on other pages, especially the program development page.
org:junk  list  links  objektbuch  data  database  error  accuracy  human-ml  machine-learning  ai  pro-rata  metrics  automation  benchmarks  marginal  nlp  language  density  writing  dataviz  meta:reading  speedometer 
may 2019 by nhaliday
python - Does pandas iterrows have performance issues? - Stack Overflow
Generally, iterrows should only be used in very very specific cases. This is the general order of precedence for performance of various operations:

1) vectorization
2) using a custom cython routine
3) apply
a) reductions that can be performed in cython
b) iteration in python space
4) itertuples
5) iterrows
6) updating an empty frame (e.g. using loc one-row-at-a-time)
q-n-a  stackex  programming  python  libraries  gotchas  data-science  sci-comp  performance  checklists  objektbuch  best-practices  DSL  frameworks 
may 2019 by nhaliday
Abbreviations | Oxford English Dictionary
This list contains the most common abbreviations used in the OED. Click on a letter to see the abbreviations beginning with that letter.

use w/ GoldenDict OED (find-in-page function is pretty much only way to filter for POS, etc., unfortunately)
language  linguistics  jargon  list  database  objektbuch  lexical 
may 2019 by nhaliday
Why is Software Engineering so difficult? - James Miller
basic message: No silver bullet!

most interesting nuggets:
Scale and Complexity
- Windows 7 > 50 million LOC
Expect a staggering number of bugs.

Bugs?
- Well-written C and C++ code contains some 5 to 10 errors per 100 LOC after a clean compile, but before inspection and testing.
- At a 5% rate any 50 MLOC program will start off with some 2.5 million bugs.

Bug removal
- Testing typically exercises only half the code.

Better bug removal?
- There are better ways to do testing that do produce fantastic programs.”
- Are we sure about this fact?
* No, its only an opinion!
* In general Software Engineering has ....
NO FACTS!

So why not do this?
- The costs are unbelievable.
- It’s not unusual for the qualification process to produce a half page of documentation for each line of code.
pdf  slides  engineering  nitty-gritty  programming  best-practices  roots  comparison  cost-benefit  software  systematic-ad-hoc  structure  error  frontier  debugging  checking  formal-methods  context  detail-architecture  intricacy  big-picture  system-design  correctness  scale  scaling-tech  shipping  money  data  stylized-facts  street-fighting  objektbuch  pro-rata  estimate  pessimism  degrees-of-freedom  volo-avolo  no-go  things  thinking  summary  quality  density  methodology 
may 2019 by nhaliday
quality - Is the average number of bugs per loc the same for different programming languages? - Software Engineering Stack Exchange
Contrary to intuition, the number of errors per 1000 lines of does seem to be relatively constant, reguardless of the specific language involved. Steve McConnell, author of Code Complete and Software Estimation: Demystifying the Black Art goes over this area in some detail.

I don't have my copies readily to hand - they're sitting on my bookshelf at work - but a quick Google found a relevant quote:

Industry Average: "about 15 - 50 errors per 1000 lines of delivered code."
(Steve) further says this is usually representative of code that has some level of structured programming behind it, but probably includes a mix of coding techniques.

Quoted from Code Complete, found here: http://mayerdan.com/ruby/2012/11/11/bugs-per-line-of-code-ratio/

If memory serves correctly, Steve goes into a thorough discussion of this, showing that the figures are constant across languages (C, C++, Java, Assembly and so on) and despite difficulties (such as defining what "line of code" means).

Most importantly he has lots of citations for his sources - he's not offering unsubstantiated opinions, but has the references to back them up.

[ed.: I think this is delivered code? So after testing, debugging, etc. I'm more interested in the metric for the moment after you've gotten something to compile.

edit: cf https://pinboard.in/u:nhaliday/b:0a6eb68166e6]
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april 2019 by nhaliday
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