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Boilerplate code
View on Wikipedia | This article needs additional citations for verification. (May 2017) |
In computer programming, boilerplate code, or simply boilerplate, are sections of code that are repeated in multiple places with little to no variation. When using languages that are considered verbose, the programmer must write a lot of boilerplate code to accomplish only minor functionality.[1]
The need for boilerplate can be reduced through high-level mechanisms such as metaprogramming (which has the computer automatically write the needed boilerplate code or insert it at compile time), convention over configuration (which provides good default values, reducing the need to specify program details in every project) and model-driven engineering (which uses models and model-to-code generators, eliminating the need for manual boilerplate code).
It is also possible to move boilerplate code to an abstract class so that it can be inherited by any number of concrete classes. Another option would be to move it into a subroutine so that it can be called instead of being duplicated.
Origin
[edit]The term arose from the newspaper business. Columns and other pieces that were distributed by print syndicates were sent to subscribing newspapers in the form of prepared printing plates. Because of their resemblance to the metal plates used in the making of boilers, they became known as "boiler plates", and their resulting text—"boilerplate text". As the stories that were distributed by boiler plates were usually "fillers" rather than "serious" news, the term became synonymous with unoriginal, repeated text.[2][3]
A related term is bookkeeping code, referring to code that is not part of the business logic but is interleaved with it in order to keep data structures updated or handle secondary aspects of the program.
Preamble
[edit]One form of boilerplate consists of declarations which, while not part of the program logic or the language's essential syntax, are added to the start of a source file as a matter of custom. The following Perl example demonstrates boilerplate:
#!/usr/bin/env perl
use warnings;
use strict;
The first line is a shebang, which identifies the file as a Perl script that can be executed directly on the command line on Unix/Linux systems. The other two are pragmas turning on warnings and strict mode, which are mandated by fashionable Perl programming style.
This next example is a C/C++ programming language boilerplate, #include guard.
#ifndef MYINTERFACE_H
#define MYINTERFACE_H
...
#endif
This checks, and sets up, a global flag to tell the compiler whether the file myinterface.h has already been included. As many interdepending files may be involved in the compilation of a module, this avoids processing the same header multiple times, (which would lead to errors due to multiple definitions with the same name).
In Java and similar platforms
[edit]In Java programs, DTO classes are often provided with methods for getting and setting instance variables. The definitions of these methods can frequently be regarded as boilerplate. Although the code will vary from one class to another, it is sufficiently stereotypical in structure that it would be better generated automatically than written by hand. For example, in the following Java class representing a pet, almost all the code is boilerplate except for the declarations of Pet, name, and owner:
Java
[edit]public class Pet {
private String name;
private Person owner;
public Pet(String name, Person owner) {
this.name = name;
this.owner = owner;
}
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
public Person getOwner() {
return owner;
}
public void setOwner(Person owner) {
this.owner = owner;
}
}
Most of the boilerplate in this example exists to fulfill requirements of JavaBeans. If the variables name and owner were declared as public, the accessor and mutator methods would not be needed.
In Java 14, record classes were added to fight with this issue.[4][5][6]
To reduce the amount of boilerplate, many frameworks have been developed, e.g. Lombok for Java.[7] The same code as above is auto-generated by Lombok using Java annotations, which is a form of metaprogramming:
@AllArgsConstructor
@Getter
@Setter
public class Pet {
private String name;
private Person owner;
}
Scala
[edit]In some other programming languages it may be possible to achieve the same thing with less boilerplate, when the language has built-in support for such common constructs. For example, the equivalent of the above Java code can be expressed in Scala using just one line of code:
case class Pet(var name: String, var owner: Person)
C#
[edit]Or in C# using automatic properties with compiler generated backing fields:
public class Pet
{
public string Name { get; set; }
public Person Owner { get; set; }
}
Starting with C# 9.0 there is an opportunity to use Records which generate classes with Properties automatically:
public record Pet(string Name, Person Owner);
HTML
[edit]In HTML, the following boilerplate is used as a basic empty template and is present in most web pages:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8"/>
<title>Test</title>
</head>
<body>
</body>
</html>
The WHATWG HTML Living Standard defines that the <html>, <head> and <body> tags may be safely omitted under most circumstances.[8] The <meta charset="UTF-8"> tag is technically redundant when coming directly from a web server configured to send the character encoding in an HTTP header, though it becomes useful when the HTML response is saved in an .html file, cache, or web archive.[9] Google's HTML/CSS style guide recommends that all optional tags be omitted,[10] resulting in much less boilerplate. The World Wide Web Consortium states that the element <title> must not be empty:[11]
<!DOCTYPE html>
<title>Test</title>
See also
[edit]- Directive (programming) – Language construct that specifies how a compiler should process its input
- General-purpose macro processor – Macro processor that is not tied to a particular language or piece of software
- "Hello, World!" program – Traditional first example of a computer programming language
- Library (computing) – Collection of resources used to develop a computer program
- Macro (computer science) – Rule for substituting a set input with a set output
- Preprocessor – Program that processes input for another program
- Scaffold (programming) – Code generation technique or a project generation technique
- Snippet (programming) – Small amount of source code used for productivity
- Template processor – Software designed to combine templates with a data model to produce result documents
- Web template system – System in web publishing
References
[edit]- ^ Lämmel, Ralf; Jones, Simon Peyton (2003). "Scrap your boilerplate: a practical design pattern for generic programming". Proceedings of the 2003 ACM SIGPLAN International Workshop on Types in Languages Design and Implementation. TLDI '03. New York: ACM. pp. 26–37. doi:10.1145/604174.604179. ISBN 9781581136494. S2CID 9472305.
- ^ "Boilerplate". Dictionary.com. Retrieved 2018-01-27.
- ^ "Boilerplate". Merriam-Webster. Retrieved 2018-01-27.
- ^ "Record Classes". docs.oracle.com.
- ^ "JEP 395: Record". openjdk.org.
- ^ Evans, Ben (2020-11-01). "Records Come to Java". blogs.oracle.com.
- ^ Frankel, Nicolas (2009-12-07). "Lombok reduces your boilerplate code". DZone.com. Retrieved 2017-08-02.
- ^ "HTML Standard - The HTML syntax - Optional tags". WHATWG. 2017-05-05. Retrieved 2017-05-05.
- ^ "Is the charset meta tag required with HTML5?". stackoverflow.com. Retrieved 2017-05-05.
- ^ "Google HTML/CSS Style Guide". google.github.io. Retrieved 2017-05-05.
- ^ "HTML page has non-empty title". www.w3.org. Retrieved 22 July 2021.
Boilerplate code
View on Grokipediaequals(), hashCode(), and toString() for data objects, which libraries like Lombok can auto-generate to reduce redundancy.[4] For web projects, HTML boilerplate includes the standard <!DOCTYPE html> declaration, <head> with meta tags, and <body> structure to ensure compatibility across browsers.[1] In database interactions, code for establishing connections, executing queries, and closing resources often follows a fixed pattern reusable across applications.[1] Various languages and frameworks mitigate boilerplate to emphasize concise, expressive code. Overall, while boilerplate ensures reliability, ongoing innovations in programming paradigms aim to balance its necessity with developer productivity.
Core Concepts
Definition
Boilerplate code refers to standardized, repetitive segments of source code that must be included in multiple locations within a program, often with little to no alteration, to satisfy the syntactic requirements of a programming language, the mandates of a framework, or conventional practices. These code portions typically lack substantial semantic meaning, serving primarily as structural necessities rather than contributing directly to the program's logic or functionality. The term, borrowed from the printing industry where it described pre-cast metal plates for reproducing standard text, entered programming lexicon by the early 1980s to denote such unvarying elements.[1][5] This code is essential for practical reasons, including appeasing compiler constraints—such as mandatory declarations or boilerplate implementations for interfaces—to ensure the program compiles successfully, initializing essential data structures to prevent runtime errors, and conforming to API contracts that enforce specific invocation patterns for interoperability. Without these elements, the code would fail to meet the language's or framework's baseline expectations, even if the core algorithmic intent is straightforward.[6][7] Boilerplate differs from related concepts like scaffolding, which involves temporary structural aids for initial project setup that can be discarded post-development, or template code, which provides a customizable skeleton for generating varied outputs. In contrast, boilerplate emphasizes obligatory verbosity and minimal adaptability, underscoring inefficiencies in languages or systems that demand extensive non-expressive code to achieve simple outcomes.[8][9]Characteristics
Boilerplate code exhibits key traits of repetitiveness, appearing identically or with minimal changes across multiple files, projects, or contexts, which distinguishes it from unique, functional logic.[1] This repetition stems from the need to adhere to language syntax, framework conventions, or standard patterns, such as class declarations or import statements.[10] Verbosity is another hallmark, as boilerplate substantially inflates code volume; for instance, defining simple data structures in statically typed languages often requires dozens of lines for getters, setters, and constructors that serve no business-specific purpose.[11] Manual copying of such code exacerbates risks, including propagation of subtle errors like mismatched parameters or overlooked updates, which can lead to defects multiplying across the codebase.[12][8] These traits impose notable impacts on software development. Repetitiveness and verbosity hinder productivity by diverting developers' focus from core functionality to rote implementation. Maintenance costs rise as changes to boilerplate—such as adapting to new API versions—must be replicated across numerous locations, increasing the likelihood of inconsistencies and bugs.[12] Furthermore, excessive boilerplate contributes to code bloat, resulting in larger, less navigable codebases that complicate debugging and onboarding.[1] On the positive side, boilerplate promotes consistency by enforcing uniform structures and compliance with established standards, reducing variability in team outputs and facilitating collaboration.[8] Metrics for identifying boilerplate often involve assessing the ratio of non-functional to functional code; in verbose languages like Java, this ratio can be substantial, underscoring its scale in enterprise applications.[13]Historical Origins
Etymology
The term "boilerplate" originated in the printing industry during the late 19th century, referring to pre-cast metal plates containing standard text—such as syndicated news stories, advertisements, or legal notices—that could be reused by newspapers without the need to reset type, enabling efficient reproduction of identical content.[14] This practice, dating back to around 1887, drew its name from the sturdy steel plates used in boiler construction, evoking the idea of rigid, unchangeable forms.[14] By the mid-20th century, particularly the 1950s, "boilerplate" had entered legal and journalistic writing to denote fixed, formulaic language in contracts, articles, or documents that required minimal modification, often criticized for its lack of originality or adaptability.[15] In technical and legal contexts, it described unchanging clauses or passages that were routinely copied into new works, streamlining production but sometimes obscuring meaning.[16] The adoption of "boilerplate" in computing followed this trajectory, emerging in the 1970s and 1980s within programming documentation and software engineering to characterize repetitive, templated blocks of code or text that programmers copied with little variation, such as initialization routines or standard declarations.[5] The earliest documented reference to "boilerplate code" specifically appears in a 1981 technical report evaluating COBOL compilers, where it highlighted fixed, reusable code segments essential yet tedious to implement manually.[5]Early Examples
In the pre-1970s era of computing, assembly language programming exemplified early forms of repetitive code essential for basic program execution. On machines like the IBM 701 (introduced in 1952), programmers had to manually encode fixed sequences of opcodes to initialize hardware components, such as loading accumulator registers with constant values and configuring memory addresses for data storage. These routines were highly standardized and reused across programs to ensure compatibility with the machine's architecture, often comprising a significant portion of the total code due to the lack of higher-level abstractions. For instance, typical assembly listings from this period show invariant setup blocks for interrupt handling and stack allocation that varied little between applications. By the 1970s, Fortran programs on mainframe systems introduced additional layers of repetitive setup through job control statements and include files. On IBM OS/360 and similar platforms, Job Control Language (JCL) statements were required to define execution environments, including dataset allocation for input/output and resource specifications like CPU time limits; these statements followed rigid, templated formats that programmers copied and minimally modified for each job submission. Include files, such as those containing common I/O subroutines (e.g., for formatted READ/WRITE operations), allowed reuse of boilerplate-like code blocks to handle device-specific formatting and error checking, reducing but not eliminating the need for manual replication in scientific computing tasks. This approach was particularly prevalent in batch processing environments where Fortran dominated numerical applications.[17] The shift toward structured programming in the late 1960s and 1970s further highlighted boilerplate elements in languages like COBOL, where verbose division headers formed a mandatory skeletal structure for all programs. COBOL required explicit declarations in sections such as the IDENTIFICATION DIVISION (for program metadata), ENVIRONMENT DIVISION (for file and device configurations), and DATA DIVISION (for record layouts), each beginning with fixed keywords and periods that provided little functional variation across business-oriented applications. These headers, often spanning multiple lines with standardized phrasing, served as precursors to contemporary templating by enforcing a uniform program architecture to facilitate readability and portability in enterprise systems.[18]Types of Boilerplate
Preambles and Imports
Preambles in programming files refer to the introductory sections that contain essential metadata or directives required for the interpreter, compiler, or parser to correctly process the subsequent code. These elements are often repetitive and standardized across projects, qualifying them as boilerplate since they must be included with minimal variation to ensure compatibility and legal compliance. Common preambles include shebang lines, encoding declarations, and license notices, which facilitate execution, character handling, and intellectual property attribution, respectively.[19] Shebang lines, also known as hashbangs, appear at the very beginning of executable script files in Unix-like operating systems to specify the path to the interpreter that should process the script. For instance, a Bash script typically starts with#!/bin/bash or #!/usr/bin/env bash to invoke the Bash shell, allowing the file to run directly without explicitly calling the interpreter from the command line. This directive is processed by the kernel's execve system call, which uses it to locate and execute the specified program with the script as its argument. Shebangs are crucial for portability across environments where default shells may vary, but they must adhere to a maximum length of 128 characters in many systems to avoid truncation issues.
Encoding declarations specify the character encoding used in source files, preventing misinterpretation of non-ASCII characters during parsing. In Python 2, for example, files containing non-ASCII characters required an explicit declaration like # -*- coding: [utf-8](/page/UTF-8) -*- within the first two lines to inform the interpreter of the encoding, as per PEP 263.[20] In Python 3, UTF-8 is the default source encoding, making such declarations optional unless a different encoding is used.[21] This boilerplate is particularly relevant in internationalized codebases, where default assumptions like ASCII could lead to syntax errors. Similarly, XML documents often begin with a prolog such as <?xml version="1.0" encoding="UTF-8"?>, which declares the XML version and encoding to guide parsers in handling the document's content accurately.[22]
License notices form another standard preamble component, embedding copyright and licensing information directly in source files to comply with open-source requirements. Projects under the Apache License 2.0, for instance, must include a boilerplate header like:
/*
* Copyright [yyyy] [name of copyright owner]
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* Copyright [yyyy] [name of copyright owner]
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include directives at the top of files incorporate header contents for type definitions and function prototypes, such as #include <iostream> to access standard input/output facilities; failure to include them leads to compilation errors due to unresolved symbols. Python uses import statements similarly, e.g., import sys or from math import sqrt, which search module paths and bind namespaces, a process that can involve dozens of lines in complex applications relying on frameworks like NumPy or Django. The purpose of these statements is fundamentally to support dependency resolution and modular composition, allowing parsers to construct the complete execution context before processing the main logic.[24][25]
Class and Method Skeletons
In object-oriented programming (OOP), class declarations frequently incorporate boilerplate code to establish fundamental structures, such as access modifiers that control visibility (e.g., public or private) and inheritance clauses that specify relationships with parent classes or interfaces. These elements enforce principles like encapsulation and polymorphism but require repetitive syntax for every class, diverting developer effort from core logic to syntactic compliance. For example, even basic classes demand explicit declarations of class names, scopes, and potential superclasses, contributing to verbosity that can overwhelm novices and inflate codebases.[26] Empty implementations within class skeletons further exemplify boilerplate, particularly for getters and setters that provide controlled access to private fields while upholding encapsulation. These methods typically consist of minimal code—such as direct field retrieval or assignment—yet must be manually written for each attribute, resulting in pairs of redundant functions that dominate data-oriented classes. In practice, this pattern scales poorly in complex hierarchies, where numerous fields necessitate dozens of such accessor methods without adding substantive behavior.[27][28] Method boilerplate manifests in required annotations, such as those indicating overrides of parent methods, which ensure type safety during inheritance but add declarative overhead to every subclass implementation. Similarly, method signatures often include exception handling stubs to declare potential throws, even for anticipated errors, alongside parameter validations like null checks that form initial guards against invalid inputs. These components promote robust error management and input integrity but generate formulaic code that repeats across methods, increasing maintenance burdens in large systems.[29][28] A hallmark of OOP boilerplate arises when filling abstract classes or implementing interfaces, where developers must supply concrete bodies for all declared method signatures, including trivial or unused ones, to satisfy compilation requirements. This exhaustive implementation mandate supports polymorphism by guaranteeing complete contracts but leads to extensive skeletal code in subclasses, especially within deep inheritance trees or multi-interface adoptions. Such patterns are prevalent in enterprise applications, where interface compliance for frameworks amplifies the volume of placeholder logic.[30][31]Examples in Programming Languages
Java and Related Languages
In Java, class and method declarations often exhibit verbosity due to the explicit specification of access modifiers, return types, parameter lists, and potential exception declarations, which contrasts with more concise syntax in other languages. For instance, defining a simple data-holding class requires manual declaration of constructors, fields with visibility controls, and supporting methods, leading to repetitive structures that obscure core logic. This verbosity is inherent to Java's design for type safety and encapsulation, as outlined in the language's access control mechanisms.[32] A prominent example of boilerplate arises from Java's checked exceptions, which compel developers to either enclose potentially throwing code in try-catch blocks or propagate them via throws clauses in method signatures. This "catch or specify" requirement ensures robust error handling but frequently results in nested or duplicated exception-handling code, particularly when methods invoke multiple I/O or resource operations. For example, reading from a file might necessitate:try (BufferedReader reader = new BufferedReader(new FileReader("file.txt"))) {
String line = reader.readLine();
// Process line
} catch (IOException e) {
// Handle exception
}
try (BufferedReader reader = new BufferedReader(new FileReader("file.txt"))) {
String line = reader.readLine();
// Process line
} catch (IOException e) {
// Handle exception
}
Serializable marker interface and manage object state persistence. Classes must typically declare a serialVersionUID field to control versioning and mark non-serializable fields as transient, alongside ensuring compatibility during deserialization. A basic serializable class might look like:
import java.io.Serializable;
public class Person implements Serializable {
private static final long serialVersionUID = 1L;
private [String](/page/String) name;
private int age;
private transient [String](/page/String) sensitiveData; // Excluded from serialization
// Constructors, getters, setters required
public Person([String](/page/String) name, int age) {
this.name = name;
this.age = age;
}
// Getters and setters...
}
import java.io.Serializable;
public class Person implements Serializable {
private static final long serialVersionUID = 1L;
private [String](/page/String) name;
private int age;
private transient [String](/page/String) sensitiveData; // Excluded from serialization
// Constructors, getters, setters required
public Person([String](/page/String) name, int age) {
this.name = name;
this.age = age;
}
// Getters and setters...
}
trait Identifiable {
def id: Int
}
case class User(name: String, override val id: Int) extends Identifiable
// Additional overrides may be needed for complex traits
trait Identifiable {
def id: Int
}
case class User(name: String, override val id: Int) extends Identifiable
// Additional overrides may be needed for complex traits
@Data produces getters, setters, equals, hashCode, and toString automatically, transforming:
import lombok.Data;
@Data
public class Person {
private String name;
private int age;
}
import lombok.Data;
@Data
public class Person {
private String name;
private int age;
}
maven-compiler-plugin with --add-opens arguments to open necessary JDK compiler modules to unnamed modules. For example, in the pom.xml file:
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.10.1</version>
<configuration>
<source>17</source>
<target>17</target>
<compilerArgs>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.code=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.comp=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.file=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.main=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.model=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.parser=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.processing=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.tree=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.util=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.jvm=ALL-UNNAMED</arg>
</compilerArgs>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.10.1</version>
<configuration>
<source>17</source>
<target>17</target>
<compilerArgs>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.code=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.comp=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.file=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.main=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.model=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.parser=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.processing=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.tree=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.util=ALL-UNNAMED</arg>
<arg>-J--add-opens=jdk.compiler/com.sun.tools.javac.jvm=ALL-UNNAMED</arg>
</compilerArgs>
</configuration>
</plugin>
mvn clean compile to resolve the compilation errors. This approach is not recommended for long-term use, as it bypasses Java's security features; instead, updating to Lombok 1.18.22 or later is preferred, as these versions include built-in support for Java 17's module system without requiring manual flags.[37][38][39]
Java records, introduced in Java 14 as a preview feature and standardized in Java 16 (March 2021), further reduce boilerplate for immutable data carrier classes by automatically generating constructors, getters, equals, hashCode, and toString methods based on the record's components. For example, a data class for a point can be defined concisely as:
public record Point(int x, int y) {}
public record Point(int x, int y) {}
C# and .NET Framework
In C# and the .NET Framework, boilerplate code often arises from the need to annotate types and members with attributes to enable framework-specific behaviors, such as serialization for data persistence or transmission. The[Serializable] attribute, applied to classes or structs, indicates that an instance can be serialized using binary or XML serializers, requiring developers to mark entire hierarchies explicitly to avoid runtime errors during object graph serialization.[41] Similarly, the [DataContract] attribute, paired with [DataMember] on properties or fields, defines contracts for serialization in scenarios like WCF services or JSON handling via DataContractJsonSerializer, enforcing opt-in serialization that demands verbose annotations for public and private members to control output format and compatibility.[42] These attributes reduce flexibility in data exposure but introduce repetitive markup, especially in domain models with complex inheritance.
.NET-specific patterns further contribute to boilerplate through interface implementations for core abstractions. Dependency injection (DI) in ASP.NET Core requires classes to implement interfaces (e.g., IService) and explicit registration in the DI container via IServiceCollection, often resulting in duplicated boilerplate for constructor injection across services, controllers, and repositories to promote loose coupling and testability.[43] Event handler registrations exemplify this in UI or domain events, where developers must declare event delegates (e.g., EventHandler), expose events on publishers, and subscribe handlers with += operators, including null checks or invocation patterns to safely multicast notifications without exceptions.[44] Async method wrappers add another layer, as converting synchronous operations to asynchronous ones involves boilerplate like Task.Run encapsulation, exception handling with try-await-catch, and ConfigureAwait calls to manage context and avoid deadlocks in UI or web scenarios.[45]
C# records, introduced in C# 9 (November 2020) with .NET 5, provide a concise way to define immutable reference types that automatically implement equality members, with support for deconstruction and pattern matching, significantly reducing boilerplate for data transfer objects compared to traditional classes. For instance:
public record Person(string FirstName, string LastName);
public record Person(string FirstName, string LastName);
public record Person(string FirstName, string LastName);
public record Person(string FirstName, string LastName);
from ... where ... select) provides syntactic sugar over extension methods like Where and Select, condensing verbose iterations into declarative expressions while maintaining compile-time type safety. For instance, a pre-LINQ loop to filter an integer list might require explicit indexing and conditionals, whereas post-3.0 LINQ reduces it to a single chained query, significantly cutting code volume in data-intensive applications.[48] Later versions, like C# 6.0's string interpolation and expression-bodied members, further streamlined LINQ integration, though core attribute and interface boilerplate persists in enterprise .NET development.
Python
Python's design philosophy emphasizes simplicity and readability, resulting in significantly less boilerplate code compared to more verbose languages like Java, where extensive class declarations and getter/setter methods are often required.[49] This minimalism aligns with the Zen of Python's principle that "simple is better than complex," allowing developers to focus on logic rather than repetitive syntax. Despite this, certain standard elements, such as imports and basic class structures, still constitute boilerplate in Python scripts and modules. Boilerplate in Python frequently begins with import statements at the module level, which bring in standard library modules, third-party packages, or future imports for compatibility. For instance,from __future__ import print_function enables Python 3-style print behavior in Python 2 code, while third-party dependencies installed via pip are typically declared in a requirements.txt file and imported explicitly, such as import [numpy](/page/NumPy) as np. These imports, though concise, must be placed at the top of files per PEP 8 style guidelines to ensure clarity and avoid namespace pollution.
For class and method setups, Python requires defining special "dunder" methods like __init__ for object initialization and __str__ for string representation, which form the basic skeleton of many classes. A minimal example is:
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
def __str__(self):
return f"Point({self.x}, {self.y})"
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
def __str__(self):
return f"Point({self.x}, {self.y})"
@dataclass decorator in Python 3.7 to automatically generate __init__, __str__, __repr__, and equality methods from type annotations, thereby reducing this boilerplate.[50][51]
In web frameworks, decorator boilerplate arises in setups like Flask's route definitions, where an application instance must be created before applying @app.route to functions, as in:
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello():
return 'Hello, World!'
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello():
return 'Hello, World!'
get and post, introducing boilerplate for common CRUD operations:
from django.views import View
class MyView(View):
def get(self, request):
return HttpResponse('GET request')
def post(self, request):
return HttpResponse('POST request')
from django.views import View
class MyView(View):
def get(self, request):
return HttpResponse('GET request')
def post(self, request):
return HttpResponse('POST request')
__call__ to ensure only one instance exists:
class SingletonMeta(type):
_instances = {}
def __call__(cls, *args, **kwargs):
if cls not in cls._instances:
instance = super().__call__(*args, **kwargs)
cls._instances[cls] = instance
return cls._instances[cls]
class Singleton(metaclass=SingletonMeta):
pass
class SingletonMeta(type):
_instances = {}
def __call__(cls, *args, **kwargs):
if cls not in cls._instances:
instance = super().__call__(*args, **kwargs)
cls._instances[cls] = instance
return cls._instances[cls]
class Singleton(metaclass=SingletonMeta):
pass
Boilerplate in Web Technologies
HTML Structures
In HTML documents, boilerplate code refers to the standardized markup required to establish a valid structure, ensuring compatibility with browsers and web standards. The basic HTML5 document begins with the<!DOCTYPE html> declaration, which instructs the browser to render the page in standards mode rather than quirks mode.[55] This is followed by the root <html> element, typically including a lang attribute for internationalization, such as <html lang="en">.
The <head> section contains essential metadata elements that provide information about the document without being rendered directly. Key components include the <title> element for the page title displayed in browser tabs, the <meta charset="UTF-8"> tag to specify character encoding for proper text rendering, and viewport meta tags like <meta name="viewport" content="width=device-width, initial-scale=1.0"> for responsive design on mobile devices. The <body> element wraps all visible content, serving as the container for the document's primary structure.
Semantic HTML elements introduce meaningful structure to boilerplate, enhancing accessibility by defining page regions for screen readers and assistive technologies. The <header> element typically encloses introductory content like logos or site titles, often creating a banner landmark role when placed as a direct child of <body>.[56] The <nav> element groups navigation links, aiding users in locating site menus, while <main> identifies the primary content area, excluding sidebars or footers to focus assistive navigation.[56] These elements comply with Web Content Accessibility Guidelines (WCAG) by providing implicit roles and improving content hierarchy without additional attributes.
HTML forms often require boilerplate for user input handling and validation to ensure data integrity before submission. Standard forms use the <form> element with attributes like action for the submission endpoint and method="post" for secure data transfer, enclosing inputs such as <input type="text" required> where the required attribute triggers built-in browser validation to prevent empty submissions. For client-side checks, the pattern attribute on inputs enforces regex-based validation, like <input type="email" pattern="[a-z0-9._%+-]+@[a-z0-9.-]+\.[a-z]{2,}$"> for email format compliance.[57]
Scripts in HTML boilerplate commonly include onload events to initialize functionality once the page loads. The <body> tag can feature an onload attribute, such as <body onload="initializeForm()">, which executes JavaScript to set up event listeners or validate elements dynamically. Inline <script> tags in the <head> or before </body> often defer execution with defer or async attributes to avoid blocking rendering, integrating with forms for real-time validation via the Constraint Validation API. This setup ensures scripts enhance rather than disrupt the document's structural boilerplate.
CSS and JavaScript Templates
In web development, CSS boilerplate often arises from the need to ensure consistent rendering across browsers, which have historically applied different default styles to HTML elements. Normalize.css serves as a widely adopted solution, functioning as a modern alternative to traditional CSS resets by selectively normalizing only those styles that require adjustment for consistency, rather than stripping all defaults.[58] This approach preserves useful browser defaults like font sizes and line heights while addressing discrepancies in elements such as lists, forms, and tables, thereby reducing the repetitive code developers must write to achieve cross-browser compatibility in projects built on HTML structures.[58] Another common form of CSS boilerplate involves vendor prefixes, which browser vendors prepend to experimental or non-standard properties to enable testing without disrupting existing code.[59] For instance, to apply a transition effect reliably across browsers, developers must include multiple prefixed versions alongside the standard property, such as-webkit-transition: all 0.3s ease; -moz-transition: all 0.3s ease; -o-transition: all 0.3s ease; transition: all 0.3s ease;, creating verbose rules that ensure compatibility in environments like Chrome, Firefox, and older Safari versions.[59] Although the reliance on prefixes has diminished with better standardization, they remain a staple in legacy codebases and for cutting-edge features.
In JavaScript, boilerplate emerges prominently in module systems and event handling, particularly for modern applications. The ES6 import statement, introduced in ECMAScript 2015, requires explicit declarations to bring in dependencies, such as import React from 'react'; to access the React library's default export, which is essential for defining components but adds repetitive syntax to every file in modular projects.[60] Similarly, setting up event listeners often involves boilerplate stubs like element.addEventListener('click', function(event) { /* handler code */ });, which must be repeated for each interactive element to capture user interactions consistently.[61]
For older browsers lacking native support, polyfills introduce additional boilerplate to emulate missing APIs, such as this implementation for addEventListener that hijacks attachEvent in Internet Explorer versions prior to 9:
(function(win, doc){
if(win.addEventListener) return;
function docHijack(p){var old = doc[p]; doc[p] = function(v){return addListen(old(v))}}
function addEvent(on, fn, self){
return (self = this).attachEvent('on' + on, function(e){
var e = e || win.event;
e.preventDefault = e.preventDefault || function(){e.returnValue = false}
e.stopPropagation = e.stopPropagation || function(){e.cancelBubble = true}
fn.call(self, e);
});
}
function addListen(obj, i){
if(i = obj.length) while(i--) obj[i].addEventListener = addEvent;
else obj.addEventListener = addEvent;
return obj;
}
addListen([doc, win]);
if('Element' in win) win.Element.prototype.addEventListener = addEvent;
else {
doc.attachEvent('onreadystatechange', function(){addListen(doc.all)});
docHijack('getElementsByTagName');
docHijack('getElementById');
docHijack('createElement');
addListen(doc.all);
}
})(window, document);
(function(win, doc){
if(win.addEventListener) return;
function docHijack(p){var old = doc[p]; doc[p] = function(v){return addListen(old(v))}}
function addEvent(on, fn, self){
return (self = this).attachEvent('on' + on, function(e){
var e = e || win.event;
e.preventDefault = e.preventDefault || function(){e.returnValue = false}
e.stopPropagation = e.stopPropagation || function(){e.cancelBubble = true}
fn.call(self, e);
});
}
function addListen(obj, i){
if(i = obj.length) while(i--) obj[i].addEventListener = addEvent;
else obj.addEventListener = addEvent;
return obj;
}
addListen([doc, win]);
if('Element' in win) win.Element.prototype.addEventListener = addEvent;
else {
doc.attachEvent('onreadystatechange', function(){addListen(doc.all)});
docHijack('getElementsByTagName');
docHijack('getElementById');
docHijack('createElement');
addListen(doc.all);
}
})(window, document);
export default function Profile() {
return (
<img
src="https://i.imgur.com/MK3eW3Am.jpg"
alt="Katherine Johnson"
/>
);
}
export default function Profile() {
return (
<img
src="https://i.imgur.com/MK3eW3Am.jpg"
alt="Katherine Johnson"
/>
);
}
import { useState } from 'react';), contributing to the repetitive scaffolding in larger applications.[63] In Angular, module declarations form a core boilerplate element through the @NgModule decorator, which organizes components, directives, and providers; a typical example is:
import { NgModule } from '@angular/core';
import { CustomMenu } from './custom-menu.component';
import { CustomMenuItem } from './custom-menu-item.component';
@NgModule({
declarations: [CustomMenu, CustomMenuItem],
exports: [CustomMenu]
})
export class CustomMenuModule { }
import { NgModule } from '@angular/core';
import { CustomMenu } from './custom-menu.component';
import { CustomMenuItem } from './custom-menu-item.component';
@NgModule({
declarations: [CustomMenu, CustomMenuItem],
exports: [CustomMenu]
})
export class CustomMenuModule { }
declarations array lists module-specific elements, enforcing a verbose configuration that scales with application complexity but introduces boilerplate for every feature module.[64]
Strategies for Reduction
Language Features and Design Choices
Programming language designs can inherently minimize boilerplate by prioritizing simplicity and flexibility, as seen in minimalist languages like Python and Ruby. Python's dynamic typing system allows variables to be assigned without explicit type declarations, enabling concise code that focuses on intent rather than ceremony. For instance, creating and using a list requires no upfront type specification:my_list = [1, 2, 3]
my_list.append(4)
my_list = [1, 2, 3]
my_list.append(4)
define_method and class_eval, permit the dynamic generation of repetitive code structures, such as attribute accessors, at runtime or class definition time. This eliminates manual repetition for common patterns; for example, instead of writing individual getter and setter methods for each attribute, a single metaprogramming block can define them all:
class Person
attr_accessor :name, :age # Metaprogramming shortcut for getters/setters
end
class Person
attr_accessor :name, :age # Metaprogramming shortcut for getters/setters
end
List<String> myList = new ArrayList<String>();
myList.add("item");
List<String> myList = new ArrayList<String>();
myList.add("item");
var keyword, allowing the compiler to deduce types from initializers while preserving static checking:
var myList = new ArrayList<String>();
myList.add("item");
var myList = new ArrayList<String>();
myList.add("item");
if err != nil blocks, which can make error-prone code dominate the logic. For a simple file read operation:
file, err := os.Open("file.txt")
if err != nil {
return err
}
defer file.Close()
data, err := io.ReadAll(file)
if err != nil {
return err
}
file, err := os.Open("file.txt")
if err != nil {
return err
}
defer file.Close()
data, err := io.ReadAll(file)
if err != nil {
return err
}
? operator have been declined to avoid complicating the language.[66]
Language evolution often involves balancing these trade-offs, as exemplified by TypeScript and Kotlin. TypeScript builds on JavaScript by introducing optional static types, requiring annotations like : string for variables and functions, which adds boilerplate absent in pure JavaScript's dynamic typing but enables early error detection:
// JavaScript
let message = "hello";
message = 123; // Allowed, runtime error if misused
// TypeScript
let message: string = "hello";
message = 123; // Compile-time error
// JavaScript
let message = "hello";
message = 123; // Allowed, runtime error if misused
// TypeScript
let message: string = "hello";
message = 123; // Compile-time error
String?) from non-nullable types and providing operators like ?. for safe chaining, which obviates Java's pervasive null checks and try-catch wrappers. A Java null check might span multiple lines:
String length = null;
if (str != null) {
length = String.valueOf(str.length());
}
String length = null;
if (str != null) {
length = String.valueOf(str.length());
}
val length = str?.length?.toString()
val length = str?.length?.toString()
Tools and Code Generation
Integrated Development Environments (IDEs) provide built-in features to automate the generation of boilerplate code, significantly reducing manual effort in creating repetitive structures like getters and setters. In IntelliJ IDEA, developers can use the "Generate" menu (accessible via Code > Generate) to automatically create constructors, getters, setters, equals, hashCode, and toString methods for Java classes, leveraging the IDE's code generation capabilities to enforce best practices such as immutability where appropriate.[69][70] Similarly, Visual Studio supports code snippets, which are reusable templates inserted via the "Insert Snippet" command or hotkeys, allowing C# developers to quickly generate property boilerplate, such as auto-implemented properties with getters and setters, directly within the editor.[71] Libraries offer annotation-based or configuration-driven approaches to eliminate boilerplate at compile or runtime. Project Lombok, a Java library, uses annotations like @Getter, @Setter, @Data, and @Builder to automatically generate accessor methods, constructors, and other common code during compilation via its integration with the Java compiler, thereby minimizing verbose class definitions without altering the bytecode.[72] For .NET applications, AutoMapper is a convention-based object mapper that automates the transformation between domain objects and data transfer objects (DTOs), replacing manual property-by-property mapping code with fluent configuration that infers mappings from naming conventions.[73] Code generators further streamline project initialization by scaffolding entire structures from templates. Yeoman, a Node.js-based scaffolding tool, employs generators—plugins run via theyo command—to create boilerplate for modern web applications, such as setting up HTML, CSS, JavaScript frameworks, and build tools like Webpack, allowing developers to bootstrap projects with predefined configurations in seconds.[74] In the Java ecosystem, Maven archetypes serve as project templates that, when invoked via the mvn archetype:generate command, produce a complete project skeleton including pom.xml, directory structures, and initial source files tailored for specific use cases like web applications or simple JARs.[75] These tools address verbosity in languages like Java and C# by automating repetitive setup, enabling focus on application logic rather than infrastructure code.