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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 software testing, test automation is the use of software separate from the software being tested to control the execution of tests and the comparison of actual outcomes with predicted outcomes. Test automation can automate some repetitive but necessary tasks in a formalized testing process already in place, or perform additional testing that would be difficult to do manually. Test automation is critical for continuous delivery and continuous testing.
In software engineering, continuous integration (CI) is the practice of merging all developers' working copies to a shared mainline several times a day. Nowadays it is typically implemented in such a way that it triggers an automated build with testing. Grady Booch first proposed the term CI in his 1991 method, although he did not advocate integrating several times a day. Extreme programming (XP) adopted the concept of CI and did advocate integrating more than once per day – perhaps as many as tens of times per day.
Release engineering, frequently abbreviated as RE or as the clipped compound Releng, is a sub-discipline in software engineering concerned with the compilation, assembly, and delivery of source code into finished products or other software components. Associated with the software release life cycle, it was said by Boris Debic of Google Inc. that release engineering is to software engineering as manufacturing is to an industrial process:
Release engineering is the difference between manufacturing software in small teams or startups and manufacturing software in an industrial way that is repeatable, gives predictable results, and scales well. These industrial style practices not only contribute to the growth of a company but also are key factors in enabling growth.
A software regression is a type of software bug where a feature that has worked before stops working. This may happen after changes are applied to the software's source code, including the addition of new features and bug fixes. They may also be introduced by changes to the environment in which the software is running, such as system upgrades, system patching or a change to daylight saving time. A software performance regression is a situation where the software still functions correctly, but performs more slowly or uses more memory or resources than before. Various types of software regressions have been identified in practice, including the following:
Build automation is the process of automating the creation of a software build and the associated processes including: compiling computer source code into binary code, packaging binary code, and running automated tests.
AnthillPro is a software tool originally developed and released as one of the first continuous integration servers. AnthillPro automates the process of building code into software projects and testing it to verify that project quality has been maintained. Software developers are able to identify bugs and errors earlier by using AnthillPro to track, collate, and test changes in real time to a collectively maintained body of computer code.
Code integrity is a measurement used in the software delivery lifecycle. It measures how high the source code's quality is when it is passed on to QA, and is affected by how thoroughly the code was processed by correctness-checking processes. Examples for such correctness-checking processes can be unit testing and integration testing, code review, test automation, AI-based code analysis etc. Code integrity is the combination of applying code correctness processes along with metrics that measure the completeness of these correctness-checking processes, such as, for example, code coverage. While code integrity is usually achieved by unit testing the source code to reach high code coverage, it is definitely not the only way, or the best way, to achieve code integrity. In fact, code coverage, a popular metric to measure the thoroughness of unit tests, is known to have a limited correlation with the measure of real code integrity.
Release management is the process of managing, planning, scheduling and controlling a software build through different stages and environments; it includes testing and deploying software releases.
DevOps is a methodology in the software development and IT industry. Used as a set of practices and tools, DevOps integrates and automates the work of software development (Dev) and IT operations (Ops) as a means for improving and shortening the systems development life cycle.
Continuous testing is the process of executing automated tests as part of the software delivery pipeline to obtain immediate feedback on the business risks associated with a software release candidate. Continuous testing was originally proposed as a way of reducing waiting time for feedback to developers by introducing development environment-triggered tests as well as more traditional developer/tester-triggered tests.
Continuous delivery (CD) is a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time and, following a pipeline through a "production-like environment", without doing so manually. It aims at building, testing, and releasing software with greater speed and frequency. The approach helps reduce the cost, time, and risk of delivering changes by allowing for more incremental updates to applications in production. A straightforward and repeatable deployment process is important for continuous delivery.
Continuous deployment (CD) is a software engineering approach in which software functionalities are delivered frequently and through automated deployments.
In software engineering, a microservice architecture is a variant of the service-oriented architecture structural style. It is an architectural pattern that arranges an application as a collection of loosely coupled, fine-grained services, communicating through lightweight protocols. One of its goals is that teams can develop and deploy their services independently of others. This is achieved by the reduction of several dependencies in the code base, allowing developers to evolve their services with limited restrictions from users, and for additional complexity to be hidden from users. As a consequence, organizations are able to develop software with fast growth and size, as well as use off-the-shelf services more easily. Communication requirements are reduced. These benefits come at a cost to maintaining the decoupling. Interfaces need to be designed carefully and treated as a public API. One technique that is used is having multiple interfaces on the same service, or multiple versions of the same service, so as to not disrupt existing users of the code.
A DevOps toolchain is a set or combination of tools that aid in the delivery, development, and management of software applications throughout the systems development life cycle, as coordinated by an organisation that uses DevOps practices.
MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous development practice of DevOps in the software field. Machine learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems. Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation, orchestration, and deployment, to health, diagnostics, governance, and business metrics. According to Gartner, MLOps is a subset of ModelOps. MLOps is focused on the operationalization of ML models, while ModelOps covers the operationalization of all types of AI models.
Static application security testing (SAST) is used to secure software by reviewing the source code of the software to identify sources of vulnerabilities. Although the process of statically analyzing the source code has existed as long as computers have existed, the technique spread to security in the late 90s and the first public discussion of SQL injection in 1998 when Web applications integrated new technologies like JavaScript and Flash.
Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations is a software engineering book co-authored by Nicole Forsgren, Jez Humble and Gene Kim. The book explores how software development teams using Lean Software and DevOps can measure their performance and the performance of software engineering teams impacts the overall performance of an organization.
TestOps refers to the discipline of managing the operational aspects of testing within the software delivery lifecycle.
References
- ↑ Sacolick, Isaac (2020-01-17). "What is CI/CD? Continuous integration and continuous delivery explained". InfoWorld . Retrieved 2021-06-01.
- ↑ "What is Continuous Development and How Does It Work? | Synopsys".
- ↑ Rossel, Sander (October 2017). Continuous Integration, Delivery, and Deployment. Packt Publishing. ISBN 978-1-78728-661-0.
- ↑ Gallaba, Keheliya (2019). "Improving the Robustness and Efficiency of Continuous Integration and Deployment". 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). pp. 619–623. doi:10.1109/ICSME.2019.00099. ISBN 978-1-7281-3094-1. S2CID 208879679.
