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# Contributor Covenant Code of Conduct
## Our Pledge
In the interest of fostering an open and welcoming environment, we as
contributors and maintainers pledge to making participation in our project and
our community a harassment-free experience for everyone, regardless of age, body
size, disability, ethnicity, sex characteristics, gender identity and expression,
level of experience, education, socio-economic status, nationality, personal
appearance, race, religion, or sexual identity and orientation.
## Our Standards
Examples of behavior that contributes to creating a positive environment
include:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or
advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic
address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable
behavior and are expected to take appropriate and fair corrective action in
response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or
reject comments, commits, code, wiki edits, issues, and other contributions
that are not aligned to this Code of Conduct, or to ban temporarily or
permanently any contributor for other behaviors that they deem inappropriate,
threatening, offensive, or harmful.
## Scope
This Code of Conduct applies both within project spaces and in public spaces
when an individual is representing the project or its community. Examples of
representing a project or community include using an official project e-mail
address, posting via an official social media account, or acting as an appointed
representative at an online or offline event. Representation of a project may be
further defined and clarified by project maintainers.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported by contacting the project team at dwmkerr@gmail.com. All
complaints will be reviewed and investigated and will result in a response that
is deemed necessary and appropriate to the circumstances. The project team is
obligated to maintain confidentiality with regard to the reporter of an incident.
Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good
faith may face temporary or permanent repercussions as determined by other
members of the project's leadership.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see
https://www.contributor-covenant.org/faq

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# Contributing Guidelines
The goal of this project is to have a set of _concise_ definitions to laws, principles, methodologies and patterns which hackers will find useful. They should be:
1. Short - one or two paragraphs.
2. Include the original source.
3. Quote the law if possible, with the author's name.
4. Link to related laws in the 'See also' section.
5. Include real-world examples if possible in the 'Real-world examples' section.
Some other tips:
- It is fine to include laws which are humorous or not serious.
- If a law does not obviously apply to development or coding, include a paragraph explaining the relevance to technologists.
- Don't worry about managing the table of contents, I can generate it.
- Feel free to include images, but aim to keep it down to one image per law.
- Be careful not to copy-and-paste content (unless it is explicitly quoted), as it might violate copyright.
- Include hyperlinks to referenced material.
- Do not advocate for the law, or aim to be opinionated on the correctness or incorrectness of the law, as this repository is simply the descriptions and links.
An example law is shown below, which covers most of the key points:
---
### The Law of Leaky Abstractions
[The Law of Leaky Abstractions on Joel on Software](https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-abstractions/)
> All non-trivial abstractions, to some degree, are leaky.
>
> (Joel Spolsky)
This law states that abstractions, which are generally used in computing to simplify working with complicated systems, will in certain situations 'leak' elements of the underlying system, this making the abstraction behave in an unexpected way.
An example might be loading a file and reading its contents. The file system APIs are an _abstraction_ of the lower level kernel systems, which are themselves an abstraction over the physical processes relating to changing data on a magnetic platter (or flash memory for an SSD). In most cases, the abstraction of treating a file like a stream of binary data will work. However, for a magnetic drive, reading data sequentially will be *significantly* faster than random access (due to increased overhead of page faults), but for an SSD drive, this overhead will not be present. Underlying details will need to be understood to deal with this case (for example, database index files are structured to reduce the overhead of random access), the abstraction 'leaks' implementation details the developer may need to be aware of.
The example above can become more complex when _more_ abstractions are introduced. The Linux operating system allows files to be accessed over a network, but represented locally as 'normal' files. This abstraction will 'leak' if there are network failures. If a developer treats these files as 'normal' files, without considering the fact that they may be subject to network latency and failures, the solutions will be buggy.
The article describing the law suggests that an over-reliance on abstractions, combined with a poor understanding of the underlying processes, actually makes dealing with the problem at hand _more_ complex in some cases.
See also:
- [Hyrum's Law](#hyrums-law-the-law-of-implicit-interfaces)
Real-world examples:
- [Photoshop Slow Startup](https://forums.adobe.com/thread/376152) - an issue I encountered in the past. Photoshop would be slow to startup, sometimes taking minutes. It seems the issue was that on startup it reads some information about the current default printer. However, if that printer is actually a network printer, this could take an extremely long time. The _abstraction_ of a network printer being presented to the system similar to a local printer caused an issue for users in poor connectivity situations.

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**Pull Request Checklist**
Please double check the items below!
- [ ] I have read the [Contributor Guidelines](./.github/contributing.md).
- [ ] I have not directly copied text from another location (unless explicitly indicated as a quote) or violated copyright.
- [ ] I have linked to the original Law.
- [ ] I have quote the law (if possible) and the author's name (if possible).
- [ ] I am happy to have my changes merged, so that I appear as a contributor, but also the text altered if required to keep the language consistent in the project.
And don't forget:
- I can handle the table of contents, feel free to leave it out.
- Check to see if other laws should link back to the law you have added.
- Include your **Twitter Handle** if you want me to include you when tweeting this update!

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Laws, Theories, Principles and Patterns that developers will find useful.
- 🇨🇳 [中文 / Chinese Version](https://github.com/nusr/hacker-laws-zh) - thanks [Steve Xu](https://github.com/nusr)!
- 🇰🇷 [한국어 / Korean Version](https://github.com/codeanddonuts/hacker-laws-kr) - thanks [Doughnut](https://github.com/codeanddonuts)!
- 🇷🇺 [Русская версия / Russian Version](https://github.com/solarrust/hacker-laws) - thanks [Alena Batitskaya](https://github.com/solarrust)!
- 🇹🇷 [Türkçe / Turkish Version](https://github.com/umutphp/hacker-laws-tr) - thanks [Umut Işık](https://github.com/umutphp)
---
<!-- vim-markdown-toc GFM -->
* [Introduction](#introduction)
* [Laws](#laws)
* [Amdahl's Law](#amdahls-law)
* [Brooks's Law](#brookss-law)
* [Brooks' Law](#brooks-law)
* [Conway's Law](#conways-law)
* [Dunbar's Number](#dunbars-number)
* [Hanlon's Razor](#hanlons-razor)
* [Hofstadter's Law](#hofstadters-law)
* [The Hype Cycle & Amara's Law](#the-hype-cycle--amaras-law)
* [Hyrum's Law (The Law of Implicit Interfaces)](#hyrums-law-the-law-of-implicit-interfaces)
* [Moore's Law](#moores-law)
* [Parkinson's Law](#parkinsons-law)
* [Premature Optimization Effect](#premature-optimization-effect)
* [Putt's Law](#putts-law)
* [The Law of Conservation of Complexity (Tesler's Law)](#the-law-of-conservation-of-complexity-teslers-law)
* [The Law of Leaky Abstractions](#the-law-of-leaky-abstractions)
@@ -22,6 +32,7 @@ Laws, Theories, Principles and Patterns that developers will find useful.
* [The Spotify Model](#the-spotify-model)
* [Wadler's Law](#wadlers-law)
* [Principles](#principles)
* [The Pareto Principle (The 80/20 Rule)](#the-pareto-principle-the-8020-rule)
* [The Robustness Principle (Postel's Law)](#the-robustness-principle-postels-law)
* [SOLID](#solid)
* [The Single Responsibility Principle](#the-single-responsibility-principle)
@@ -29,6 +40,9 @@ Laws, Theories, Principles and Patterns that developers will find useful.
* [The Liskov Substitution Principle](#the-liskov-substitution-principle)
* [The Interface Segregation Principle](#the-interface-segregation-principle)
* [The Dependency Inversion Principle](#the-dependency-inversion-principle)
* [The DRY Principle](#the-dry-principle)
* [YAGNI](#yagni)
* [Reading List](#reading-list)
* [TODO](#todo)
<!-- vim-markdown-toc -->
@@ -57,28 +71,31 @@ The diagram below shows some examples of potential improvements in speed:
*(Image Reference: By Daniels220 at English Wikipedia, Creative Commons Attribution-Share Alike 3.0 Unported, https://en.wikipedia.org/wiki/File:AmdahlsLaw.svg)*
As can be seen, even a program which is 50% parallelisable will benefit very little beyond 10 processing units, where as a program which is 95% parallelisable can still achieve significant speed improvements with over a thousand processing units.
As can be seen, even a program which is 50% parallelisable will benefit very little beyond 10 processing units, whereas a program which is 95% parallelisable can still achieve significant speed improvements with over a thousand processing units.
As [Moore's Law](#moores-law) slows, and the acceleration of individual processor speed slows, parallelisation is key to improving performance. Graphics programming is an excellent example - with modern Shader based computing, individual pixels or fragments can be rendered in parallel - this is why modern graphics cards often have many thousands of processing cores (GPUs or Shader Units).
See also:
- [Brooks's Law](#brookss-law)
- [Brooks' Law](#brooks-law)
- [Moore's Law](#moores-law)
### Brooks's Law
### Brooks' Law
[Brooks's Law on Wikipedia](https://en.m.wikipedia.org/wiki/Brooks%27s_law)
[Brooks' Law on Wikipedia](https://en.m.wikipedia.org/wiki/Brooks%27s_law)
> Adding human resources to a late software development project makes it later.
This law suggests that in many cases, attempting to accelerate the delivery of a project which is already late, by adding more people, will make the delivery even later. Brooks is clear that this is an over-simplification, however the general reasoning is that given the ramp up time of new resources and the communication overheads, in the immediate short-term velocity decreases. Also, many tasks may not be divisible, i.e. easily distributed between more resources, meaning the potential velocity increase is also lower.
This law suggests that in many cases, attempting to accelerate the delivery of a project which is already late, by adding more people, will make the delivery even later. Brooks is clear that this is an over-simplification, however, the general reasoning is that given the ramp up time of new resources and the communication overheads, in the immediate short-term velocity decreases. Also, many tasks may not be divisible, i.e. easily distributed between more resources, meaning the potential velocity increase is also lower.
The common phrase in delivery "Nine women can't make a baby in one month" relates to Brooks's Law, in particular the fact that some kinds of work are not divisible or parallelisable.
The common phrase in delivery "Nine women can't make a baby in one month" relates to Brooks' Law, in particular, the fact that some kinds of work are not divisible or parallelisable.
This is a central theme of the book '[The Mythical Man Month](#reading-list)'.
See also:
- [Death March](#todo)
- [Reading List: The Mythical Man Month](#reading-list)
### Conway's Law
@@ -90,21 +107,55 @@ See also:
- [The Spotify Model](#the-spotify-model)
### Dunbar's Number
[Dunbar's Number on Wikipedia](https://en.wikipedia.org/wiki/Dunbar%27s_number)
"Dunbar's number is a suggested cognitive limit to the number of people with whom one can maintain stable social relationships— relationships in which an individual knows who each person is and how each person relates to every other person." There is some disagreement to the exact number. "... [Dunbar] proposed that humans can comfortably maintain only 150 stable relationships." He put the number into a more social context, "the number of people you would not feel embarrassed about joining uninvited for a drink if you happened to bump into them in a bar." Estimates for the number generally lay between 100 and 250.
Like stable relationships between individuals, a developer's relationship with a codebase takes effort to maintain. When faced with large complicated projects, or ownership of many projects we lean on convention, policy, and modeled procedure to scale. Dunbar's number is not only important to keep in mind as an office grows, but also when setting the scope for team efforts or deciding when a system should invest in tooling to assist in modeling and automating logistical overhead. Putting the number into an engineering context, it is the number of projects (or normalized complexity of a single project) for which you would feel confident in joining an on-call rotation to support.
See also:
- [Conway's Law](#conways-law)
### Hanlon's Razor
[Hanlon's Razor on Wikipedia](https://en.wikipedia.org/wiki/Hanlon%27s_razor)
> Never attribute to malice that which is adequately explained by stupidity.
>
> Robert J. Hanlon
This principle suggests that actions resulting in a negative outcome were not a result of ill will. Instead the negative outcome is more likely attributed to those actions and/or the impact being not fully understood.
### Hofstadter's Law
[Hofstadter's Law on Wikipedia](https://en.wikipedia.org/wiki/Hofstadter%27s_law)
> It always takes longer than you expect, even when you take into account Hofstadter's Law.
>
> (Douglas Hofstadter)
You might hear this law referred to when looking at estimates for how long something will take. It seems a truism in software development that we tend to not be very good at accurately estimating how long something will take to deliver.
This is from the book '[Gödel, Escher, Bach: An Eternal Golden Braid](#reading-list)'.
See also:
- [Reading List: Gödel, Escher, Bach: An Eternal Golden Braid](#reading-list)
### Hutber's Law
[Hutber's Law on Wikipedia](https://en.wikipedia.org/wiki/Hutber%27s_law)
> Improvement means deterioration.
>
> ([Patrick Hutber](https://en.wikipedia.org/wiki/Patrick_Hutber))
This law suggests that improvements to a system will lead to deterioration in other parts, or it will hide other deterioration, leading overall to a degredation from the current state of the system. For example, a decrease in response latency for a particular end-point could cause increased throughput and capacity issues further along in a request flow, effecting an entirely different sub-system.
This law suggests that improvements to a system will lead to deterioration in other parts, or it will hide other deterioration, leading overall to a degredation from the current state of the system.
For example, a decrease in response latency for a particular end-point could cause increased throughput and capacity issues further along in a request flow, effecting an entirely different sub-system.
### The Hype Cycle & Amara's Law
@@ -133,11 +184,12 @@ In short, this cycle suggests that there is typically a burst of excitement arou
>
> (Hyrum Wright)
Hyrum's Law states that when you have a _large enough number of consumers_ of an API, all behaviours of the API (even those not defined as part of a public contract) will eventually come to be depended on by someone. A trivial examples may be non-functional elements such as the response time of an API. A more subtle example might be consumers who are relying on applying a regex to an error message to determine the *type* of error of an API. Even if the public contract of the API states nothing about the contents of the message, indicating users should use an associated error code, _some_ users may use the message, and changing the message essentially breaks the API for those users.
Hyrum's Law states that when you have a _large enough number of consumers_ of an API, all behaviours of the API (even those not defined as part of a public contract) will eventually come to be depended on by someone. A trivial example may be non-functional elements such as the response time of an API. A more subtle example might be consumers who are relying on applying a regex to an error message to determine the *type* of error of an API. Even if the public contract of the API states nothing about the contents of the message, indicating users should use an associated error code, _some_ users may use the message, and changing the message essentially breaks the API for those users.
See also:
- [The Law of Leaky Abstractions](#the-law-of-leaky-abstractions)
- [XKCD 1172](https://xkcd.com/1172/)
### Moore's Law
@@ -145,7 +197,7 @@ See also:
> The number of transistors in an integrated circuit doubles approximately every two years.
Often used to illustrate the sheer speed at which semiconductor and chip technology has improved, Moore's prediction has proven to be highly accurate over from the 1970s to the late 2000s. In more recent years, the trend has changed slightly, partly due to [physical limitations on the degree to which components can be miniaturised](https://en.wikipedia.org/wiki/Quantum_tunnelling). However, advancements in parallelisation, and potentially revolutionary changes in semi-conductor technology and quantum computing may mean that Moore's Law could continue to hold true for decades to come.
Often used to illustrate the sheer speed at which semiconductor and chip technology has improved, Moore's prediction has proven to be highly accurate over from the 1970s to the late 2000s. In more recent years, the trend has changed slightly, partly due to [physical limitations on the degree to which components can be miniaturised](https://en.wikipedia.org/wiki/Quantum_tunnelling). However, advancements in parallelisation, and potentially revolutionary changes in semiconductor technology and quantum computing may mean that Moore's Law could continue to hold true for decades to come.
### Parkinson's Law
@@ -153,14 +205,26 @@ Often used to illustrate the sheer speed at which semiconductor and chip technol
> Work expands so as to fill the time available for its completion.
In it's original context, this Law was based on studies of bureaucracies. It may be pessimistically applied to software development initiatives, the theory being that teams will be inefficient until deadlines near, then rush to complete work by the deadline, thus making the actual deadline somewhat arbitrary.
In its original context, this Law was based on studies of bureaucracies. It may be pessimistically applied to software development initiatives, the theory being that teams will be inefficient until deadlines near, then rush to complete work by the deadline, thus making the actual deadline somewhat arbitrary.
If this law were combined with [Hofstadter's Law](#hofstadters-law), an even more pessimistic viewpoint is reached - work will expand to fill the time available for it's completion and *still take longer than expected*.
If this law were combined with [Hofstadter's Law](#hofstadters-law), an even more pessimistic viewpoint is reached - work will expand to fill the time available for its completion and *still take longer than expected*.
See also:
- [Hofstadter's Law](#hofstadters-law)
### Premature Optimization Effect
[Premature Optimization on WikiWikiWeb](http://wiki.c2.com/?PrematureOptimization)
> Premature optimization is the root of all evil.
>
> [(Donald Knuth)](https://twitter.com/realdonaldknuth?lang=en)
In Donald Knuth's paper [Structured Programming With Go To Statements](http://wiki.c2.com/?StructuredProgrammingWithGoToStatements), he wrote: "Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: **premature optimization is the root of all evil**. Yet we should not pass up our opportunities in that critical 3%."
However, _Premature Optimization_ can be defined (in less loaded terms) as optimizing before we know that we need to.
### Putt's Law
[Putt's Law on Wikipedia](https://en.wikipedia.org/wiki/Putt%27s_Law_and_the_Successful_Technocrat)
@@ -177,7 +241,7 @@ However, it should be stressed that Laws such as this are vast generalisations a
See also:
- [The Peter Principe](#TODO)
- [The Peter Principle](#TODO)
- [Dilbert's Law](#TODO).
@@ -197,13 +261,13 @@ One interesting element to this law is the suggestion that even by simplifying t
> All non-trivial abstractions, to some degree, are leaky.
>
> (Joel Spolsky)
> ([Joel Spolsky](https://twitter.com/spolsky))
This law states that abstractions, which are generally used in computing to simplify working with complicated systems, will in certain situations 'leak' elements of the underlying system, this making the abstraction behave in an unexpected way.
An example might be loading a file and reading its contents. The file system APIs are an _abstraction_ of the lower level kernel systems, which are themselves an abstraction over the physical processes relating to changing data on a magnetic platter (or flash memory for an SSD). In most cases, the abstraction of treating a file like a stream of binary data will work. However, for a magnetic drive, reading data sequentially will be *significantly* faster than random access (due to increased overhead of page faults), but for an SSD drive, this overhead will not be present. Underlying details will need to be understood to deal with this case (for example, database index files are structured to reduce the overhead of random access), the abstraction 'leaks' implementation details the developer may need to be aware of.
The example above can become more complex when _more_ abstractions are introduced. The Linux operating system allows files to be accessed over a network, but represented locally as 'normal' files. This abstraction will 'leak' if there are network failures. If a developer treats these files as 'normal' files, without considering the fact that they may be subject to network latency and failures, the solutions will be buggy.
The example above can become more complex when _more_ abstractions are introduced. The Linux operating system allows files to be accessed over a network but represented locally as 'normal' files. This abstraction will 'leak' if there are network failures. If a developer treats these files as 'normal' files, without considering the fact that they may be subject to network latency and failures, the solutions will be buggy.
The article describing the law suggests that an over-reliance on abstractions, combined with a poor understanding of the underlying processes, actually makes dealing with the problem at hand _more_ complex in some cases.
@@ -229,9 +293,9 @@ The fictional example above led to the usage of the term 'Bike Shedding' as an e
[The Unix Philosophy on Wikipedia](https://en.wikipedia.org/wiki/Unix_philosophy)
The Unix Philosophy is that software components should be small, and focused on doing one specific thing well. This can make it easier to build systems by composing together small, simple, well defined units, rather than using large, complex, multi-purpose programs.
The Unix Philosophy is that software components should be small, and focused on doing one specific thing well. This can make it easier to build systems by composing together small, simple, well-defined units, rather than using large, complex, multi-purpose programs.
Modern practices like 'Microservice Architecture' can be thought of as an application of this law, where services are small, focused and do one specific thing, allowing complex behaviour to be composed from simple building blocks.
Modern practices like 'Microservice Architecture' can be thought of as an application of this law, where services are small, focused and do one specific thing, allowing complex behaviour to be composed of simple building blocks.
### The Spotify Model
@@ -264,6 +328,28 @@ See also:
Principles are generally more likely to be guidelines relating to design.
### The Pareto Principle (The 80/20 Rule)
[The Pareto Principle on Wikipedia](https://en.wikipedia.org/wiki/Pareto_principle)
> Most things in life are not distributed evenly.
The Pareto Principle suggests that in some cases, the majority of results come from a minority of inputs:
- 80% of a certain piece of software can be written in 20% of the total allocated time (conversely, the hardest 20% of the code takes 80% of the time)
- 20% of the effort produces 80% of the result
- 20% of the work creates 80% of the revenue
- 20% of the bugs cause 80% of the crashes
- 20% of the features cause 80% of the usage
In the 1940s American-Romanian engineer Dr. Joseph Juran, who is widely credited with being the father of quality control, [began to apply the Pareto principle to quality issues](https://en.wikipedia.org/wiki/Joseph_M._Juran).
This principle is also known as: The 80/20 Rule, The Law of the Vital Few and The Principle of Factor Sparsity.
Real-world examples:
- In 2002 Microsoft reported that by fixing the top 20% of the most-reported bugs, 80% of the related errors and crashes in windows and office would become eliminated ([Reference](https://www.crn.com/news/security/18821726/microsofts-ceo-80-20-rule-applies-to-bugs-not-just-features.htm)).
### The Robustness Principle (Postel's Law)
[The Robustness Principle on Wikipedia](https://en.wikipedia.org/wiki/Robustness_principle)
@@ -294,11 +380,11 @@ These are key principles in [Object-Oriented Programming](#todo). Design princip
The first of the '[SOLID](#solid)' principles. This principle suggests that modules or classes should do one thing and one thing only. In more practical terms, this means that a single, small change to a feature of a program should require a change in one component only. For example, changing how a password is validated for complexity should require a change in only one part of the program.
Theoretically this should make code more robust, and easier to change. Knowing that a component which is being changed has a single responsibility only means that _testing_ that change should be easier. Using the earlier example, changing the password complexity component should only be able to affect the features which relate to password complexity. It can be much more difficult to reason about the impact of a change to a component which has many responsibilities.
Theoretically, this should make the code more robust, and easier to change. Knowing that a component which is being changed has a single responsibility only means that _testing_ that change should be easier. Using the earlier example, changing the password complexity component should only be able to affect the features which relate to password complexity. It can be much more difficult to reason about the impact of a change to a component which has many responsibilities.
See also:
- [Object-Orientated Programming](#todo)
- [Object-Oriented Programming](#todo)
- [SOLID](#solid)
### The Open/Closed Principle
@@ -315,7 +401,7 @@ This principle has particular relevance for object-oriented programming, where w
See also:
- [Object-Orientated Programming](#todo)
- [Object-Oriented Programming](#todo)
- [SOLID](#solid)
### The Liskov Substitution Principle
@@ -326,13 +412,13 @@ See also:
The third of the '[SOLID](#solid)' principles. This principle states that if a component relies on a type, then it should be able to use subtypes of that type, without the system failing or having to know the details of what that subtype is.
As an example, imagine we have a method which reads an XML document from a structure which represents a file. If the method uses a base type 'file', then anything which derives from 'file' should be able to be used in the function. If 'file' supports seeking in reverse, and the xml parser uses that function, but the derived type 'network file' fails when reverse seeking is attempted, then the 'network file' would be violating the principle.
As an example, imagine we have a method which reads an XML document from a structure which represents a file. If the method uses a base type 'file', then anything which derives from 'file' should be able to be used in the function. If 'file' supports seeking in reverse, and the XML parser uses that function, but the derived type 'network file' fails when reverse seeking is attempted, then the 'network file' would be violating the principle.
This principle has particular relevance for object-orientated programming, where type hierarchies must be modelled carefully to avoid confusing users of a system.
This principle has particular relevance for object-oriented programming, where type hierarchies must be modeled carefully to avoid confusing users of a system.
See also:
- [Object-Orientated Programming](#todo)
- [Object-Oriented Programming](#todo)
- [SOLID](#solid)
### The Interface Segregation Principle
@@ -343,22 +429,22 @@ See also:
The fourth of the '[SOLID](#solid)' principles. This principle states that consumers of a component should not depend on functions of that component which it doesn't actually use.
As an example, imagine we have a method which reads an XML document from structure which represents a file. It only needs to read bytes, move forwards or move backwards in the file. If this method needs to be updated because an unrelated feature of the file structure changes (such as an update to the permissions model used to represent file security), then the principle has been invalidated. It would be better for the file to implement a 'seekable-stream' interface, and for the XML reader to use that.
As an example, imagine we have a method which reads an XML document from a structure which represents a file. It only needs to read bytes, move forwards or move backwards in the file. If this method needs to be updated because an unrelated feature of the file structure changes (such as an update to the permissions model used to represent file security), then the principle has been invalidated. It would be better for the file to implement a 'seekable-stream' interface, and for the XML reader to use that.
This principle has particular relevance for object-orientated programming, where interfaces, hierarchies and abstract types are used to [minimise the coupling](#todo) between different components. [Duck typing](#todo) is a methodology which enforces this principle by eliminating explicit interfaces.
This principle has particular relevance for object-oriented programming, where interfaces, hierarchies and abstract types are used to [minimise the coupling](#todo) between different components. [Duck typing](#todo) is a methodology which enforces this principle by eliminating explicit interfaces.
See also:
- [Object-Orientated Programming](#todo)
- [Object-Oriented Programming](#todo)
- [SOLID](#solid)
- [Duck Typing](#todo)
- [Decoupling](#todo)
### The Dependency Inversion Principle
[The Dependency Inversion Principle](https://en.wikipedia.org/wiki/Dependency_inversion_principle)
[The Dependency Inversion Principle on Wikipedia](https://en.wikipedia.org/wiki/Dependency_inversion_principle)
> High level modules should not be dependent on low-level implementations.
> High-level modules should not be dependent on low-level implementations.
The fifth of the '[SOLID](#solid)' principles. This principle states that higher level orchestrating components should not have to know the details of their dependencies.
@@ -368,13 +454,56 @@ This principle is complex, as it can seem to 'invert' the expected dependencies
See also:
- [Object-Orientated Programming](#todo)
- [Object-Oriented Programming](#todo)
- [SOLID](#solid)
- [Inversion of Control](#todo)
- [Dependency Injection](#todo)
### The DRY Principle
[The DRY Principle on Wikipedia](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself)
> Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.
DRY is an acronym for _Don't Repeat Yourself_. This principle aims to help developers reducing the repetition of code and keep the information in a single place and was cited in 1999 by Andrew Hunt and Dave Thomas in the book [The Pragmatic Developer](https://en.wikipedia.org/wiki/The_Pragmatic_Programmer)
> The opposite of DRY would be _WET_ (Write Everything Twice or We Enjoy Typing).
In practice, if you have the same piece of information in two (or more) different places, you can use DRY to merge them into a single one and reuse it wherever you want/need.
See also:
- [The Pragmatic Developer](https://en.wikipedia.org/wiki/The_Pragmatic_Programmer)
### YAGNI
[YAGNI on Wikipedia](https://en.wikipedia.org/wiki/You_aren%27t_gonna_need_it)
This is an acronym for _**Y**ou **A**ren't **G**onna **N**eed **I**t_.
> Always implement things when you actually need them, never when you just foresee that you need them.
>
> ([Ron Jeffries](https://twitter.com/RonJeffries)) (XP co-founder and author of the book "Extreme Programming Installed")
This _Extreme Programming_ (XP) principle suggests developers should only implement functionality that is needed for the immediate requirements, and avoid attempts to predict the future by implementing functionality that might be needed later.
Adhering to this principle should reduce the amount of unused code in the codebase, and avoid time and effort being wasted on functionality that brings no value.
See also:
- [Reading List: Extreme Programming Installed](#reading-list)
## Reading List
If you have found these concepts interesting, you may enjoy the following books.
- [Extreme Programming Installed - Ron Jeffries, Ann Anderson, Chet Hendrikson](https://www.goodreads.com/en/book/show/67834) - Covers the core principles of Extreme Programming.
- [The Mythical Man Month - Frederick P. Brooks Jr.](https://www.goodreads.com/book/show/13629.The_Mythical_Man_Month) - A classic volume on software engineering. [Brooks' Law](#brooks-law) is a central theme of the book.
- [Gödel, Escher, Bach: An Eternal Golden Braid - Douglas R. Hofstadter.](https://www.goodreads.com/book/show/24113.G_del_Escher_Bach) - This book is difficult to classify. [Hofstadter's Law](#hofstadters-law) is from the book.
## TODO
Hi! If you land here, you've clicked on a link to a topic I've not written up yet, sorry about this - this is work in progress!
Feel free to [Raise an Issue](https://github.com/dwmkerr/hacker-laws/issues) requesting more details, or [Open a Pull Request](https://github.com/dwmkerr/hacker-laws/pulls) to submit your proposed definition of the topic.
Feel free to [Raise an Issue](https://github.com/dwmkerr/hacker-laws/issues) requesting more details, or [Open a Pull Request](https://github.com/dwmkerr/hacker-laws/pulls) to submit your proposed definition of the topic.