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description=A collection of principles and patterns that shape software systems, teams, and decisions.;
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laws of software engineering this site works best with javascript enabled search and filtering require javascript skip to main content laws of software engineering newsletter info book poster cards laws of software engineering a collection of principles and patterns that shape software systems teams and decisions 56 laws click any card to learn more print the card deck y 1 2k points on hacker news level all junior mid level senior category all architecture teams planning quality scale design decisions teams conway s law organizations design systems that mirror their own communication structure planning premature optimization knuth s optimization principle premature optimization is the root of all evil architecture hyrum s law with a sufficient number of api users all observable behaviors of your system will be depended on by somebody quality the boy scout rule leave the code better than you found it design yagni you aren t gonna need it don t add functionality until it is necessary teams brooks s law adding manpower to a late software project makes it later architecture gall s law a complex system that works is invariably found to have evolved from a simple system that worked architecture the law of leaky abstractions all non trivial abstractions to some degree are leaky architecture tesler s law conservation of complexity every application has an inherent amount of irreducible complexity that can only be shifted not eliminated c a p architecture cap theorem a distributed system can guarantee only two of consistency availability and partition tolerance architecture second system effect small successful systems tend to be followed by overengineered bloated replacements architecture fallacies of distributed computing a set of eight false assumptions that new distributed system designers often make architecture law of unintended consequences whenever you change a complex system expect surprise architecture zawinski s law every program attempts to expand until it can read mail 150 teams dunbar s number there is a cognitive limit of about 150 stable relationships one person can maintain teams the ringelmann effect individual productivity decreases as group size increases n teams price s law the square root of the total number of participants does 50 of the work teams putt s law those who understand technology don t manage it and those who manage it don t understand it teams peter principle in a hierarchy every employee tends to rise to their level of incompetence 1 teams bus factor the minimum number of team members whose loss would put the project in serious trouble teams dilbert principle companies tend to promote incompetent employees to management to limit the damage they can do planning parkinson s law work expands to fill the time available for its completion 90 90 planning the ninety ninety rule the first 90 of the code accounts for the first 90 of development time the remaining 10 accounts for the other 90 planning hofstadter s law it always takes longer than you expect even when you take into account hofstadter s law planning goodhart s law when a measure becomes a target it ceases to be a good measure planning gilb s law anything you need to quantify can be measured in some way better than not measuring it quality murphy s law sod s law anything that can go wrong will go wrong quality postel s law be conservative in what you do be liberal in what you accept from others quality broken windows theory don t leave broken windows bad designs wrong decisions or poor code unrepaired quality technical debt technical debt is everything that slows us down when developing software quality linus s law given enough eyeballs all bugs are shallow quality kernighan s law debugging is twice as hard as writing the code in the first place quality testing pyramid a project should have many fast unit tests fewer integration tests and only a small number of ui tests quality pesticide paradox repeatedly running the same tests becomes less effective over time quality lehman s laws of software evolution software that reflects the real world must evolve and that evolution has predictable limits quality sturgeon s law 90 of everything is crap scale amdahl s law the speedup from parallelization is limited by the fraction of work that cannot be parallelized scale gustafson s law it is possible to achieve significant speedup in parallel processing by increasing the problem size scale metcalfe s law the value of a network is proportional to the square of the number of users 1 design dry don t repeat yourself every piece of knowledge must have a single unambiguous authoritative representation design kiss keep it simple stupid designs and systems should be as simple as possible design solid principles five main guidelines that enhance software design making code more maintainable and scalable a b c design law of demeter an object should only interact with its immediate friends not strangers design principle of least astonishment software and interfaces should behave in a way that least surprises users and other developers decisions dunning kruger effect the less you know about something the more confident you tend to be decisions hanlon s razor never attribute to malice that which is adequately explained by stupidity or carelessness decisions occam s razor the simplest explanation is often the most accurate one decisions sunk cost fallacy sticking with a choice because you ve invested time or energy in it even when walking away helps you map real decisions the map is not the territory our representations of reality are not the same as reality itself decisions confirmation bias a tendency to favor information that supports our existing beliefs or ideas decisions the hype cycle amara s law we tend to overestimate the effect of a technology in the short run and underestimate the impact in the long run decisions the lindy effect the longer something has been in use the more likely it is to continue being used decisions first principles thinking breaking a complex problem into its most basic blocks and then building up from there decisions inversion solving a problem by considering the opposite outcome and working backward from it 80 20 decisions pareto principle 80 20 rule 80 of the problems result from 20 of the causes decisions cunningham s law the best way to get the correct answer on the internet is not to ask a question it s to post the wrong answer no laws found try a different search term or clear filters a collection of principles and patterns that shape software systems teams and decisions 2026 dr milan milanović license privacy newsletter info book poster cards errata