Happy path

Last updated

In the context of software or information modeling, a happy path (sometimes called happy flow) is a default scenario featuring no exceptional or error conditions. [1] [2] For example, the happy path for a function validating credit card numbers would be where none of the validation rules raise an error, thus letting execution continue successfully to the end, generating a positive response.

Contents

Process steps for a happy path are also used in the context of a use case. In contrast to the happy path, process steps for alternate flow and exception flow may also be documented. [3]

Happy path test is a well-defined test case using known input, which executes without exception and produces an expected output. [4] Happy path testing can show that a system meets its functional requirements but it doesn't guarantee a graceful handling of error conditions or aid in finding hidden bugs. [5] [4]

Happy day (or sunny day) scenario and golden path are slang synonyms for happy path. [6]

In use case analysis, there is only one happy path, but there may be any number of additional alternate path scenarios which are all valid optional outcomes. If valid alternatives exist, the happy path is then identified as the default or most likely positive alternative. The analysis may also show one or more exception paths. An exception path is taken as the result of a fault condition. Use cases and the resulting interactions are commonly modeled in graphical languages such as the Unified Modeling Language (UML) or SysML. [7]

Unhappy path

There is no agreed name for the opposite of happy paths: they may be known as sad paths, bad paths, or exception paths. The term 'unhappy path' is gaining popularity as it suggests a complete opposite to 'happy path' and retains the same context. Usually there is no extra 'unhappy path', leaving such 'term' meaningless, because the happy path reaches the utter end, but an 'unhappy path' is shorter, ends prematurely, and doesn't reach the desired end, i.e. not even the last page of a wizard. And in contrast to a single happy path, there are a lot of different ways in which things can go wrong, so there is no single criterion to determine 'the unhappy path'.

See also

Related Research Articles

In software engineering, code coverage is a percentage measure of the degree to which the source code of a program is executed when a particular test suite is run. A program with high test coverage has more of its source code executed during testing, which suggests it has a lower chance of containing undetected software bugs compared to a program with low test coverage. Many different metrics can be used to calculate test coverage. Some of the most basic are the percentage of program subroutines and the percentage of program statements called during execution of the test suite.

Software testing is the act of examining the artifacts and the behavior of the software under test by validation and verification. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but are not necessarily limited to:

In computing and computer programming, exception handling is the process of responding to the occurrence of exceptions – anomalous or exceptional conditions requiring special processing – during the execution of a program. In general, an exception breaks the normal flow of execution and executes a pre-registered exception handler; the details of how this is done depend on whether it is a hardware or software exception and how the software exception is implemented. Exception handling, if provided, is facilitated by specialized programming language constructs, hardware mechanisms like interrupts, or operating system (OS) inter-process communication (IPC) facilities like signals. Some exceptions, especially hardware ones, may be handled so gracefully that execution can resume where it was interrupted.

In computer programming, unit testing is a software testing method by which individual units of source code—sets of one or more computer program modules together with associated control data, usage procedures, and operating procedures—are tested to determine whether they are fit for use.

Quantitative marketing research is the application of quantitative research techniques to the field of marketing research. It has roots in both the positivist view of the world, and the modern marketing viewpoint that marketing is an interactive process in which both the buyer and seller reach a satisfying agreement on the "four Ps" of marketing: Product, Price, Place (location) and Promotion.

In software and systems engineering, the phrase use case is a polyseme with two senses:

  1. A usage scenario for a piece of software; often used in the plural to suggest situations where a piece of software may be useful.
  2. A potential scenario in which a system receives an external request and responds to it.

Test-driven development (TDD) is a software development process relying on software requirements being converted to test cases before software is fully developed, and tracking all software development by repeatedly testing the software against all test cases. This is as opposed to software being developed first and test cases created later.

<span class="mw-page-title-main">Requirements analysis</span> Engineering process

In systems engineering and software engineering, requirements analysis focuses on the tasks that determine the needs or conditions to meet the new or altered product or project, taking account of the possibly conflicting requirements of the various stakeholders, analyzing, documenting, validating and managing software or system requirements.

White-box testing is a method of software testing that tests internal structures or workings of an application, as opposed to its functionality. In white-box testing, an internal perspective of the system is used to design test cases. The tester chooses inputs to exercise paths through the code and determine the expected outputs. This is analogous to testing nodes in a circuit, e.g. in-circuit testing (ICT). White-box testing can be applied at the unit, integration and system levels of the software testing process. Although traditional testers tended to think of white-box testing as being done at the unit level, it is used for integration and system testing more frequently today. It can test paths within a unit, paths between units during integration, and between subsystems during a system–level test. Though this method of test design can uncover many errors or problems, it has the potential to miss unimplemented parts of the specification or missing requirements. Where white-box testing is design-driven, that is, driven exclusively by agreed specifications of how each component of software is required to behave, white-box test techniques can accomplish assessment for unimplemented or missing requirements.

Hard coding is the software development practice of embedding data directly into the source code of a program or other executable object, as opposed to obtaining the data from external sources or generating it at runtime. Hard-coded data typically can only be modified by editing the source code and recompiling the executable, although it can be changed in memory or on disk using a debugger or hex editor. Data that are hard-coded is best for unchanging pieces of information, such as physical constants, version numbers and static text elements. Softcoded data, on the other hand, encode arbitrary information through user input, text files, INI files, HTTP server responses, configuration files, preprocessor macros, external constants, databases, command-line arguments, and are determined at runtime.

<span class="mw-page-title-main">Model-based testing</span>

Model-based testing is an application of model-based design for designing and optionally also executing artifacts to perform software testing or system testing. Models can be used to represent the desired behavior of a system under test (SUT), or to represent testing strategies and a test environment. The picture on the right depicts the former approach.

In software engineering, a test case is a specification of the inputs, execution conditions, testing procedure, and expected results that define a single test to be executed to achieve a particular software testing objective, such as to exercise a particular program path or to verify compliance with a specific requirement. Test cases underlie testing that is methodical rather than haphazard. A battery of test cases can be built to produce the desired coverage of the software being tested. Formally defined test cases allow the same tests to be run repeatedly against successive versions of the software, allowing for effective and consistent regression testing.

In computing, a scenario is a narrative of foreseeable interactions of user roles and the technical system, which usually includes computer hardware and software.

Data cleansing or data cleaning is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting or a data quality firewall.

Dynamic program analysis is analysis of computer software that involves executing the program in question. Dynamic program analysis includes familiar techniques from software engineering such as unit testing, debugging, and measuring code coverage, but also includes lesser-known techniques like program slicing and invariant inference. Dynamic program analysis is widely applied in security in the form of runtime memory error detection, fuzzing, dynamic symbolic execution, and taint tracking.

<span class="mw-page-title-main">Misuse case</span>

Misuse case is a business process modeling tool used in the software development industry. The term Misuse Case or mis-use case is derived from and is the inverse of use case. The term was first used in the 1990s by Guttorm Sindre of the Norwegian University of Science and Technology, and Andreas L. Opdahl of the University of Bergen, Norway. It describes the process of executing a malicious act against a system, while use case can be used to describe any action taken by the system.

<span class="mw-page-title-main">WebCL</span>

WebCL is a JavaScript binding to OpenCL for heterogeneous parallel computing within any compatible web browser without the use of plug-ins, first announced in March 2011. It is developed on similar grounds as OpenCL and is considered as a browser version of the latter. Primarily, WebCL allows web applications to actualize speed with multi-core CPUs and GPUs. With the growing popularity of applications that need parallel processing like image editing, augmented reality applications and sophisticated gaming, it has become more important to improve the computational speed. With these background reasons, a non-profit Khronos Group designed and developed WebCL, which is a Javascript binding to OpenCL with a portable kernel programming, enabling parallel computing on web browsers, across a wide range of devices. In short, WebCL consists of two parts, one being Kernel programming, which runs on the processors (devices) and the other being JavaScript, which binds the web application to OpenCL. The completed and ratified specification for WebCL 1.0 was released on March 19, 2014.

High performance computing applications run on massively parallel supercomputers consist of concurrent programs designed using multi-threaded, multi-process models. The applications may consist of various constructs with varying degree of parallelism. Although high performance concurrent programs use similar design patterns, models and principles as that of sequential programs, unlike sequential programs, they typically demonstrate non-deterministic behavior. The probability of bugs increases with the number of interactions between the various parallel constructs. Race conditions, data races, deadlocks, missed signals and live lock are common error types.

Verification and validation of computer simulation models is conducted during the development of a simulation model with the ultimate goal of producing an accurate and credible model. "Simulation models are increasingly being used to solve problems and to aid in decision-making. The developers and users of these models, the decision makers using information obtained from the results of these models, and the individuals affected by decisions based on such models are all rightly concerned with whether a model and its results are "correct". This concern is addressed through verification and validation of the simulation model.

Negative testing is a method of testing an application or system that ensures that the plot of the application is according to the requirements and can handle the unwanted input and user behavior. Invalid data is inserted to compare the output against the given input. Negative testing is also known as failure testing or error path testing. When performing negative testing exceptions are expected. This shows that the application is able to handle improper user behavior. Users input values that do not work in the system to test its ability to handle incorrect values or system failure.

References

  1. ↑ BPMN: A Meta Model for the Happy Path
  2. ↑ Meszaros, Gerard. "happy path". xUnit Patterns. Archived from the original on 2017-10-19. Retrieved 2018-02-16.
  3. ↑ "Alternate & Exception Flow".
  4. 1 2 Edwards, Stephen H.; Shams, Zalia (2014). "Do student programmers all tend to write the same software tests?". Proceedings of the 2014 conference on Innovation & technology in computer science education - ITiCSE '14. pp. 171–176. doi:10.1145/2591708.2591757. ISBN   9781450328333. S2CID   18184659.{{cite book}}: CS1 maint: date and year (link)
  5. ↑ Cohen, Julie; Plakosh, Dan; Keeler, Kristi (2005). Robustness Testing of Software-Intensive Systems: Explanation and Guide (Technical report). Carnegie Mellon University, Software Engineering Institute. doi:10.1184/R1/6583508.v1. CMU/SEI-2005-TN-015.
  6. ↑ "Cambridge Dictionary".
  7. ↑ Flexible Views for View-based Model-driven Development By Burger, Erik. KIT Scientific Publishing, Nov 14, 2014. Pg. 250.


This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.