The Testing, Tools, and Frameworks Zone encapsulates one of the final stages of the SDLC as it ensures that your application and/or environment is ready for deployment. From walking you through the tools and frameworks tailored to your specific development needs to leveraging testing practices to evaluate and verify that your product or application does what it is required to do, this Zone covers everything you need to set yourself up for success.
Microservices architecture is an increasingly popular approach to building complex, distributed systems. In this architecture, a large application is divided into smaller, independent services that communicate with each other over the network. Microservices testing is a crucial step in ensuring that these services work seamlessly together. This article will discuss the importance of microservices testing, its challenges, and best practices. Importance of Microservices Testing Testing microservices is critical to ensuring that the system works as intended. Unlike traditional monolithic applications, microservices are composed of small, independent services that communicate with each other over a network. As a result, microservices testing is more complex and challenging than testing traditional applications. Nevertheless, testing is crucial to detect issues and bugs in the system, improve performance, and ensure that the microservices work correctly and efficiently. Microservices testing is critical for ensuring a microservices-based application's reliability, scalability, and maintainability. Here are some reasons why microservices testing is essential: Independent Testing: Each microservice is an independent unit, which means that it can be tested separately. This makes testing easier and more efficient. Increased Agility: Testing each microservice separately allows for faster feedback and faster development cycles, leading to increased agility. Scalability: Microservices can be scaled horizontally, which means that you can add more instances of a service to handle increased traffic. However, this requires proper testing to ensure that the added instances are working correctly. Continuous Integration and Delivery: Microservices testing can be integrated into continuous integration and delivery pipelines, allowing for automatic testing and deployment. Challenges of Microservices Testing Testing microservices can be challenging due to the following reasons: Integration Testing: Testing the interaction between multiple microservices can be challenging because of the large number of possible interactions. Network Issues: Microservices communicate with each other over the network, which can introduce issues related to latency, network failure, and data loss. Data Management: In a microservices architecture, data is often distributed across multiple services, making it difficult to manage and test. Dependency Management: Microservices can have many dependencies, which can make testing complex and time-consuming. Best Practices for Microservices Testing Here are some best practices for microservices testing: Test Each Microservice Separately: Each microservice should be tested separately to ensure that it works as expected. Since microservices are independent services, it is essential to test each service independently. This allows you to identify issues specific to each service and ensure that each service meets its requirements. Use Mocks and Stubs: Use mocks and stubs to simulate the behavior of other services that a service depends on. Mock services are useful for testing microservices that depend on other services that are not available for testing. Mock services mimic the behavior of the missing services and allow you to test the microservices in isolation. Automate Testing: Automate testing as much as possible to speed up the process and reduce human error. Automated testing is essential in a microservices architecture. It allows you to test your system repeatedly, quickly, and efficiently. Automated testing ensures that each service works independently and that the system functions correctly as a whole. Automated testing also helps to reduce the time and effort required for testing. Use Chaos Engineering: Use chaos engineering to test the resilience of your system in the face of unexpected failures. Test Data Management: Test data management and ensure that data is consistent across all services. Use Containerization: Use containerization, such as Docker, to create an isolated environment for testing microservices. Test Service Integration: While testing each service independently is crucial, it is equally important to test service integration. This ensures that each service can communicate with other services and that the system works as a whole. In addition, integration testing is critical to detecting issues related to communication and data transfer. Test for Failure: Failure is inevitable, and microservices are no exception. Testing for failure is critical to ensure that the system can handle unexpected failures, such as server crashes, network failures, or database errors. Testing for failure helps to improve the resilience and robustness of the system. Conclusion Microservices testing is a critical step in ensuring the reliability, scalability, and maintainability of microservices-based applications. Proper testing helps to identify issues early in the development cycle, reducing the risk of costly failures in production. Testing each microservice separately, automating testing, testing each service independently, testing service integration, testing for failure, and using mocks and stubs are some best practices for microservice testing. By following these best practices, you can ensure that your microservices-based application is reliable and scalable. In addition, implementing these best practices can help to improve the reliability, resilience, and robustness of your microservices architecture.
After being voted as the best programming language in the year 2018, Python still continues rising up the charts and currently ranks as the third best programming language just after Java and C, as per the index published by Tiobe. With the increasing use of this language, the popularity of test automation frameworks based on Python is increasing as well. Obviously, developers and testers will get a little bit confused when it comes to choosing the best framework for their project. While choosing one, you should judge a lot of things, the script quality of the framework, test case simplicity and the technique to run the modules and find out their weaknesses. This is my attempt to help you compare the top five Python frameworks for test automation in 2019, and their advantages and disadvantages over the other frameworks. So you could choose the ideal Python framework for test automation according to your needs. Robot Framework Used mostly for development that is acceptance test-driven as well as for acceptance testing, the Robot Framework is one of the top Python test frameworks. Although it is developed using Python, it can also run on IronPython, which is .net-based and on Java-based Jython. Robot as a Python framework is compatible across all platforms—Windows, MacOS, or Linux. Prerequisites First of all, you will be able to use Robot Framework (RF), only when you have Python 2.7.14 or any version above it installed. Although, Python 3.6.4 is also used, code snippets provided in the official blog of RF will make sure the appropriate notes are added, consisting of all the changes required. You will also need to install “pip” or Python package manager. Finally, a development framework is a must to download. A popular framework among developers is the PyCharm Community Edition. However, since code snippets are not IDE-dependent, you can use any IDE, which you have worked on earlier. Advantages and Disadvantages of Robot Let’s take a look at the advantages and disadvantages of Robot as a test automation framework over other Python frameworks: Pros Using a keyword-driven-test approach, it makes the automation process simpler by helping the testers easily create readable test cases. Test data syntax can be used easily. Consisting of generic tools and test libraries, it has a vast ecosystem where individual elements can be used in separate projects. The framework is highly extensible since it has many APIs. The Robot framework helps you run parallel tests via a Selenium Grid; however, this feature is not built in. Cons The Robot framework although is tricky when it comes to creating customized HTML reports. However, you could still present xUnit formatted short reports by using the Robot framework. Another flaw of the Robot framework is the inadequacy of parallel testing. Is Robot the Top Python Test Framework for You? If you are a beginner in the automation domain and have less experience in development, using Robot as a top Python test framework is easier to use than Pytest or Pyunit, since it has rich built libraries and involves using an easier test-oriented DSL. However, if you want to develop a complex automation framework, it is better to switch to Pytest or any other framework involving Python code. Pytest Used for all kinds of software testing, Pytest is another top Python test framework for test automation. Being open source and easy to learn, the tool can be used by QA teams, development teams, individual practice groups, and in open source projects. Because of its useful features, like “assert rewriting,” most projects on the internet, including big shots like Dropbox and Mozilla, have switched from unittest (Pyunit) to Pytest. Let’s take a deep dive and find out what’s so special about this Python framework. Prerequisites Apart from a working knowledge in Python, Pytest does not need anything complex. All you need is a working desktop that has: A command line interface Python package manager IDE for development Advantages and Disadvantages of Pytest Pros In the Python testing community, before the arrival of Pytest, developers included their tests inside large classes. However, a revolution was brought by Pytest since it made it possible to write test suites in a very compact manner than before. Other testing tools require the developer, or tester, to use a debugger or check the logs and detect where a certain value is coming from. Pytest helps you write test cases in a way that gives you the ability to store all values inside the test cases and inform you which value failed and which value is asserted. The tests are easier to write and understand since the boilerplate code is not needed that much. Fixtures are functions you can use by adding an argument to your test function. Their job is to return values. In Pytest, you can make them modular by using one fixture from another. Using multiple fixtures helps you cover all the parameter combinations without rewriting test cases. Developers of Pytest released some useful plugins that make the framework extensible. For example, pytest-xdist can be used to execute parallel testing without using a different test runner. Unit tests can also be parameterized without duplicating any code. Provides developers with certain special routines that make test case writing simpler and less prone to errors. The code also becomes shorter and easily understandable. Cons The fact that special routines are used by Pytest means you have to compromise with compatibility. You will be able to conveniently write test cases but won’t be able to use those test cases with any other testing framework. Is Pytest the Top Python Test Framework for You? Well, you have to start by learning a full-fledged language, but once you get the hang of it, you will get all the features like static code analysis, support for multiple IDE, and, most importantly, writing effective test cases. For writing functional test cases and developing a complex framework, it is better than unittest but its advantage is somewhat similar to the Robot framework if your aim is to develop a simple framework. UnitTest (PyUnit) Unittest, or PyUnit, is the standard test automation framework for unit testing that comes with Python. It’s highly inspired by JUnit. The assertion methods and all the cleanup and setup routines are provided by the base class TestCase. The name of each and every method in the subclass of TestCase starts with “test.” This allows them to run as test cases. You can use the load methods and TestSuite class to the group and load the tests. Together, you can use them to build customized test runners. Like Selenium testing with JUnit, unittest also has the ability to use unittest-sml-reporting and generate XML reports. Prerequisites There are no such prerequisites since unittest comes by default with Python. To use it, you will need standard knowledge of the Python framework and if you want to install additional modules, you will need pip installed along with an IDE for development. Advantages and Disadvantages of PyUnit Pros Being part of the standard library of Python, there are several advantages of using Unittest: The developers are not required to install any additional module since it comes with the box. Unittest is xUnit’s derivative and its working principle is similar to other xUnit frameworks. People who do not have a strong background in Python generally find it comfortable to work. You can run individual test cases in a simpler manner. All you need to do is specify the names on the terminal. The output is concise as well, making the framework flexible when it comes to executing test cases. The test reports are generated within milliseconds. Cons Usually, snake_case is used for naming Python codes. But, since this framework is inspired a lot from Junit, the traditional camelCase naming method persists. This can be quite confusing. The intent of the test code sometimes become unclear, since it supports abstraction too much. A huge amount of boilerplate code is required. Is PyUnit the Top Python Test Framework for You? As per my personal opinion and the opinion of other Python developers, Pytest introduced certain idioms, which allowed testers to write better automation code in a very compact manner. Although unittest comes as a default test automation framework, the fact that its working principle and naming conventions are a bit different than standard Python codes, and its requirement of too many boilerplate codes, make it a not so preferred Python test automation framework. Behave We are all aware of behavior driven development, the latest agile-based software development methodology that encourages developers, business participants, and quality analysts to collaborate among each other. Behave is another one of the top Python test frameworks that allows the team to execute BDD testing without any complications. The nature of this framework is quite similar to SpecFlow and Cucumber for automation testing. Test cases are written in a simple readable language and later stuck to the code during execution. The behavior is designed by the behavior specs and the steps are then reused by other test scenarios. Prerequisites Anyone with basic knowledge in Python should be able to use Behave. Let’s take a look at the prerequisites: Before installing Behave, you have to install any version of Python above 2.7.14. Python package manager, or pip, is required to work with Behave. A development environment is the last and most important thing you need. You can use Pycharm, which is preferred by most developers, or any other IDE of your choice. Advantages and Disadvantages of Behave Like all other behavior in driven test frameworks, the opinion regarding Behave’s advantage varies from person to person. Let’s take a look at the common pros and cons of using Behave: Pros System behavior is expressed by semi-formal language and a domain vocabulary that keeps the behavior consistent in the organization. Dev teams who are working on different modules with similar features are properly coordinated. Building blocks are always ready for executing all kinds of test cases. Reasoning and thinking are featured in details, resulting in better product specs. Stakeholders or managers have a better clarity regarding the output of QAs and devs because of the similar format of the specs. Cons The only disadvantage is that it works well only for black box testing. Is Behave the Top Python Test Framework for You? Well, as we said, Behave (Python framework) works best only for black box testing. Web testing is a great example since use cases can be described in plain language. However, for integration testing or unit testing, Behave is not a good choice since the verbosity will only cause complications for complex test scenarios. Developers, as well as testers, recommend pytest-bdd. It is an alternative to Behave since it uses all that is good in Pytest and implements it for testing a behavior driven scenario. Lettuce Lettuce is another simple and easy to use behavior driven automation tool based on Cucumber and Python. The main objective of Lettuce is to focus on the common tasks of behavior driven development, making the process simpler and entertaining. Prerequisites You will need, at minimum, Python 2.7.14 installed along with an IDE. You can use Pycharm or any other IDE of your choice. Also, for running tests, you will be required to install the Python package manager. Advantages and Disadvantages of Lettuce Pros Well, just like any other BDD testing framework, Lettuce enables developers to create more than one scenario and describe the features in the simple natural language. Dev and QA teams are properly coordinated since the specs are of similar format. For black box testing, Lettuce is quite useful for running behavior driven tests cases. Cons There is only one disadvantage of using Lettuce as a Python framework. For successful execution of behavior driven tests, communication is necessary between dev teams, QA, and stakeholders. Absence or communication gap will make the process ambiguous and questions can be raised from any team. Is Lettuce the Top Python Test Framework for You? According to developers and automation testers, Cucumber is more useful when it comes to executing BDD tests. However, if we are talking about Python developers and QA, there is no better replacement than pytest-bdd. All the great features of Pytest, like compactness and easy to understand code, are implemented in this framework combined with the verbosity of behavior driven testing. Wrapping Up! In the above article, we have discussed the top five Python frameworks for test automation in 2019, based on different testing procedures. While Pytest, Robot framework, and unittest are meant for functional and unit testing, Lettuce and Behave are best for behavior driven testing only. From the features stated, we can conclude for functional testing, Pytest is the best. But if you are new to Python-based automation testing, the Robot framework is a great tool to get started. Although the features are limited, it will enable you to get ahead on the track easily. For Python-based BDD testing, Lettuce and Behave are equally good, but if you already have experience with Pytest, it’s better to use pytest-bdd. I hope my article helps you make the right choice out of the top Python test frameworks for your Python web automation needs. Happy testing!
Shift-left testing is a software testing approach where testing is moved to an earlier phase in the development process, closer to the development phase. The goal of shift-left testing is to catch and fix defects as early as possible in the development cycle, which can save time and resources in the long run. A real-time example of shift-left testing is in a microservices architecture where each service is developed and tested independently before being integrated with other services. For example, My team is developing a payment page for an airline platform that involves a new microservice for managing shopping carts and order services. The development team begins by writing unit tests for the cart and order service, which test the individual functions and methods of the service. Once these tests pass, the team can proceed to integration testing, where both services (cart and order) are tested against other services in the platform to ensure that it works as expected. Once the development and testing of both services (cart and order) are completed, it is then deployed to a staging environment where it is tested again. If there are any issues found in staging, they are resolved, and the service is deployed to production. By performing testing early in the development process, the team can catch and fix defects early on, saving time and resources that would have been spent on later stages of testing. It’s also important to note that shift-left testing is not only about testing earlier but also about involving the whole team in the testing process, where developers, QA, and ops collaborate to test, identify and fix issues, which leads to a more streamlined and efficient development process. Adopting Shift Left Testing in a Software Development Lifecycle The software development life cycle (SDLC) is a process followed in an organization for a software project. It consists of various stages, starting from planning, designing, developing, testing, deploying, and maintaining software. It is a framework that outlines the steps and activities involved in developing software, from the initial planning stages to the final deployment and maintenance of the software. Adopting Shift left testing in a software development lifecycle improves the quality of the software and reduces the time and cost required to fix defects later in the process. Below are some ways in which you can adopt to shift left testing in your organization: 1. Involve Testers Earlier in the Development Process Testers should be involved in the development process as early as possible to provide feedback and help identify defects. This involves working closely with developers, attending daily stand-ups, and participating in design and code reviews. To implement shift left testing, organizations often follow Agile Methodology and have sprint ceremonies such as sprint grooming and sprint planning where both QA and Development teams are involved from the beginning. During this time, QA can ask clarifying questions about requirements and provide inputs as well. 2. Implement BDD/TDD Approach This approach has several benefits. The test cases prepared by QA can help developers think about scenarios they may not have considered. Additionally, QA may identify cases that were missed by the product owner, business analyst, or the person who is responsible for gathering requirements. Identifying potential issues and creating test cases early in the development process can save time and effort later on. Without this early identification, issues may not be discovered until later stages of development or testing, at which point it may be more time-consuming and costly to make changes to address them. 3. Encourage Developers to Write Unit Testing Unit testing involves testing individual units or components of code to ensure that they are working correctly. It is an important shift left testing technique that can be used to identify and fix defects early in the development process. You can provide training on how to write effective unit tests, as well as tools and frameworks that can be used to automate unit testing. 4. Conducting Internal Demo Conducting an internal demo to the sprint team on the sprint closure day is an effective way to implement shift left testing. During this demo, team members can visually see the work completed in the previous sprint, including any changes or updates to the website or product. This allows them to provide feedback and identify potential issues early on rather than waiting for formal testing to be conducted later in the process. By involving the entire team in the demo, you can increase the chances of identifying potential issues and gathering valuable feedback. This can help improve the quality and value of the product, as it will not only be tested by a dedicated tester at a later stage but also reviewed and assessed by the entire team. This task ensures all relevant scenarios are considered, and necessary changes are made before the product is released. 5. Monitor Test Coverage Use tools to monitor test coverage or the percentage of code that is tested to ensure that you are testing all relevant code. Using code coverage tools can help you monitor test coverage and also helps in analyzing your codebase and reports on the percentage of code that is covered by tests. 6. Use Version Control and Code Review Use version control systems, such as Git, to track changes to your codebase and enable collaboration. Use code review to ensure that code is reviewed and tested by multiple team members before it is deployed.For example, you might set up a code review process in which all new code is reviewed by at least one other team member before it is merged into the main codebase. This can help identify and fix defects early on and improve the overall quality of your software. By incorporating these techniques into your shift left testing strategy, you can effectively identify and fix defects early in the development process, improving the quality and efficiency of your software 7. Automation Testing Automated testing can be used to test individual units or components of code as they are being developed, allowing you to identify defects early on. This can help reduce the time and effort required for testing later on and improve the quality of your software by identifying and fixing defects early in the development cycle.For example, when developing microservices, you can use automated testing to perform component testing early in the development process. By preparing test cases based on the Swagger or Confluence page and calling the service directly from the feature branch, you can verify that the service is working as intended. You can also write code in the same branch as the development team and check classes or enumerations that are being used to ensure that they meet the requirements. By performing early testing, you can identify bugs and defects at an early stage of software development, improving the quality and efficiency of your development process. 8. Testing Every Component Testing every component is an important aspect of shift left testing. If you are testing an API and not all of the API is developed, you can still test what is available and mock the response for the rest. Using concepts such as stubs and drivers, you can focus on testing the ready components without worrying about what is not yet available. This can give you confidence that the developed components are working correctly. Later, when the entire API is available for testing, you can quickly verify its functionality without spending a lot of time on testing that has already been covered earlier. Additionally, you can concentrate on your API’s functioning in connection to other third-party APIs it communicates with during this testing phase. You can make sure your API is operating properly and consistently by evaluating various third-party API behaviors. 9. Include Security Testing Security testing should be integrated into the development process as early as possible to identify and fix security vulnerabilities early on. This can involve using tools such as static analysis tools, dynamic analysis tools, and penetration testing tools to test the security of the software. These tools can be used to test the security of the software early in the development process. How Shift Left Testing Beneficial? Below are some of the points in which shift left testing proves to be beneficial:Reduced time and cost: By starting testing earlier in the development process, organizations can catch defects earlier and reduce the time and cost of testing. Improved Quality: By testing early and often, organizations can identify and fix defects before they become more complex and expensive. Enhanced Collaboration: Shift-left testing encourages collaboration between development and testing teams, which can improve communication and lead to a better understanding of the requirements and design of the software. Greater Agility: Shift left testing can help organizations be more agile and responsive to changes in the market or business requirements, as it allows them to quickly identify and fix defects and make changes to the software. Conclusion Shift Left testing is not a new approach, but it has gained more popularity in recent years as organizations have sought to improve the efficiency and effectiveness of their software development processes.It is a valuable approach that can help organizations improve the quality of their software and reduce the time and cost required to develop it.
The rapid growth of technology has led to an increased demand for efficient and effective software testing methods. One of the most promising advancements in this field is the integration of Natural Language Processing (NLP) techniques. NLP, a subset of artificial intelligence (AI), is focused on the interaction between computers and humans through natural language. In the context of software testing, NLP offers the potential to automate test case creation and documentation, ultimately reducing the time, effort, and costs associated with manual testing processes. This article explores the benefits and challenges of using NLP in software testing, focusing on automating test case creation and documentation. We will discuss the key NLP techniques used in this area, real-world applications, and the future of NLP in software testing. Overview of Natural Language Processing (NLP) NLP is an interdisciplinary field that combines computer science, linguistics, and artificial intelligence to enable computers to understand, interpret, and generate human language. This technology has been used in various applications such as chatbots, voice assistants, sentiment analysis, and machine translation. The primary goal of NLP is to enable computers to comprehend and process large amounts of natural language data, making it easier for humans to interact with machines. NLP techniques can be classified into two main categories: rule-based and statistical-based approaches. Rule-based approaches rely on predefined linguistic rules and patterns, while statistical approaches utilize machine learning algorithms to learn from data. NLP in Software Testing Traditionally, software testing has been a labor-intensive and time-consuming process that requires a deep understanding of the application's functionality and the ability to identify and report potential issues. Testers must create test cases, execute them, and document the results in a clear and concise manner. With the increasing complexity of modern software applications, the manual approach to software testing becomes even more challenging and error-prone. NLP has the potential to revolutionize software testing by automating test case creation and documentation. By leveraging NLP techniques, testing tools can understand the requirements and specifications written in natural language, automatically generating test cases and maintaining documentation. Automating Test Case Creation NLP can be used to automate the generation of test cases by extracting relevant information from requirement documents or user stories. The main NLP techniques involved in this process include: Tokenization: The process of breaking down a text into individual words or tokens, making it easier to analyze and process the text. Part-of-speech (POS) tagging: Assigning grammatical categories (such as nouns, verbs, adjectives, etc.) to each token in a given text. Dependency parsing: Identifying the syntactic structure and relationships between the tokens in a text. Named entity recognition (NER): Detecting and categorizing entities (such as people, organizations, locations, etc.) in a text. Semantic analysis: Extracting the meaning and context from the text to understand the relationships between the entities and actions described in the requirements or user stories. By using these techniques, NLP-based tools can process natural language inputs and automatically generate test cases based on the identified entities, actions, and conditions. This not only reduces the time and effort needed for test case creation but also helps in ensuring that all relevant scenarios are covered, minimizing the chances of missing critical test cases. Automating Test Documentation One of the key aspects of software testing is maintaining accurate and up-to-date documentation that outlines test plans, test cases, and test results. This documentation is crucial for understanding the state of the application and ensuring that all requirements have been met. However, manually maintaining test documentation can be time-consuming and error-prone. NLP can be used to automate test documentation by extracting relevant information from test cases and test results and generating human-readable reports. This process may involve the following NLP techniques: Text summarization: Creating a condensed version of the input text, highlighting the key points while maintaining the original meaning. Text classification: Categorizing the input text based on predefined labels or criteria, such as the severity of a bug or the status of a test case. Sentiment analysis: Analyzing the emotional tone or sentiment expressed in the text, which can be useful for understanding user feedback or bug reports. Document clustering: Grouping similar documents together, making it easier to organize and navigate the test documentation. By automating the documentation process, NLP-based tools can ensure that the test documentation is consistently up-to-date and accurate, reducing the risk of miscommunication or missed issues. Real-World Applications Several organizations have already started incorporating NLP into their software testing processes, with promising results. Some examples of real-world applications include: IBM's Requirements Quality Assistant (RQA) RQA is an AI-powered tool that uses NLP techniques to analyze requirement documents and provide suggestions for improving their clarity, consistency, and completeness. By leveraging NLP, RQA can help identify potential issues early in the development process, reducing the likelihood of costly rework and missed requirements. Testim.io Testim is an end-to-end test automation platform that uses NLP and machine learning to generate, execute, and maintain tests for web applications. By understanding the application's user interface (UI) elements and their relationships, Testim can automatically create test cases based on real user interactions, ensuring comprehensive test coverage. QTest by Tricentis QTest is an AI-driven test management tool that incorporates NLP techniques to automate the extraction of test cases from user stories or required documents. QTest can identify entities, actions, and conditions within the text and generate test cases accordingly, streamlining the test case creation process. Challenges and Future Outlook While NLP holds great promise for automating test case creation and documentation, there are still challenges to overcome. One major challenge is the ambiguity and complexity of natural language. Requirements and user stories can be written in various ways, with different levels of detail and clarity, making it difficult for NLP algorithms to consistently extract accurate and relevant information. Additionally, the accuracy and efficiency of NLP algorithms depend on the quality and quantity of the training data. As software testing is a domain-specific area, creating high-quality training data sets can be challenging and time-consuming. Despite these challenges, the future outlook for NLP in software testing remains optimistic. As NLP algorithms continue to improve and mature, it is expected that the integration of NLP in software testing tools will become more widespread. Moreover, the combination of NLP with other AI techniques, such as reinforcement learning and computer vision, has the potential to further enhance the capabilities of automated testing solutions. Summary Natural Language Processing (NLP) offers a promising approach to automating test case creation and documentation in software testing. By harnessing the power of NLP techniques, software testing tools can efficiently process and understand requirements written in natural language, automatically generate test cases, and maintain up-to-date documentation. This has the potential to significantly reduce the time, effort, and costs associated with traditional manual testing processes. Real-world applications, such as IBM's RQA, Testim.io, and QTest by Tricentis, have demonstrated the value of incorporating NLP into software testing workflows. However, there are still challenges to be addressed, such as the ambiguity and complexity of natural language and the need for high-quality training data. As NLP algorithms continue to advance and improve, it is anticipated that the role of NLP in software testing will expand and become more prevalent. Combining NLP with other AI techniques may further enhance the capabilities of automated testing solutions, leading to even more efficient and effective software testing processes. To summarise, the integration of Natural Language Processing (NLP) in software testing holds great promise for improving the efficiency and effectiveness of test case creation and documentation. Furthermore, as technology continues to evolve and mature, it is expected to play an increasingly important role in the future of software testing, ultimately transforming the way we test and develop software applications.
In this article, I will look at Specification by Example (SBE) as explained in Gojko Adzic’s book of the same name. It’s a collaborative effort between developers and non-developers to arrive at textual specifications that are coupled to automatic tests. You may also have heard of it as behavior-driven development or executable specifications. These are not synonymous concepts, but they do overlap. It's a common experience in any large, complex project. Crucial features do not behave as intended. Something was lost in the translation between intention and implementation, i.e., business and development. Inevitably we find that we haven’t built quite the right thing. Why wasn’t this caught during testing? Obviously, we’re not testing enough, or the wrong things. Can we make our tests more insightful? Some enthusiastic developers and SBE adepts jump to the challenge. Didn’t you know you can write all your tests in plain English? Haven’t you heard of Gherkin's syntax? She demonstrates the canonical Hello World of executable specifications, using Cucumber for Java. Gherkin Scenario: Items priced 100 euro or more are eligible for 5% discount for loyal customers Given Jill has placed three orders in the last six months When she looks at an item costing 100 euros or more Then she is eligible for a 5% discount Everybody is impressed. The Product Owner greenlights a proof of concept to rewrite the most salient test in Gherkin. The team will report back in a month to share their experiences. The other developers brush up their Cucumber skills but find they need to write a lot of glue code. It’s repetitive and not very DRY. Like the good coders they are, they make it more flexible and reusable. Gherkin Scenario: discount calculator for loyal customers Given I execute a POST call to /api/customer/12345/orders?recentMonths=6 Then I receive a list of 3 OrderInfo objects And a DiscountRequestV1 message for customer 12345 is put on the queue /discountcalculator [ you get the message ] Reusable yes, readable, no. They’re right to conclude that the textual layer offers nothing, other than more work. It has zero benefits over traditional code-based tests. Business stakeholders show no interest in these barely human-readable scripts, and the developers quickly abandon the effort. It’s About Collaboration, Not Testing The experiment failed because it tried to fix the wrong problem. It failed because better testing can’t repair a communication breakdown between getting from the intended functionality to implementation. SBE is about collaboration. It is not a testing approach. You need this collaboration to arrive at accurate and up-to-date specifications. To be clear, you always have a spec (like you always have an architecture). It may not always be a formal one. It can be a mess that only exists in your head, which is only acceptable if you’re a one-person band. In all other cases, important details will get lost or confused in the handover between disciplines. The word handover has a musty smell to it, reminiscent of old-school Waterfall: the go-to punchbag for everything we did wrong in the past, but also an antiquated approach that few developers under the age of sixty have any real experience with it. Today we’re Agile and multi-disciplinary. We don’t have specialists who throw documents over the fence of their silos. It is more nuanced than that, now as well as in 1975. Waterfall didn’t prohibit iteration. You could always go back to an earlier stage. Likewise, the definition of a modern multi-disciplinary team is not a fungible collection of Jacks and Jills of all trades. Nobody can be a Swiss army knife of IT skills and business domain knowledge. But one enduring lesson from the past is that we can’t produce flawless and complete specifications of how the software should function, before writing its code. Once you start developing, specs always turn out over-complete, under-complete, and just plain wrong in places. They have bugs, just like code. You make them better with each iteration. Accept that you may start off incomplete and imprecise. You Always Need a Spec Once we have built the code according to spec (whatever form that takes), do we still need that spec, as an architect’s drawing after the house was built? Isn’t the ultimate truth already in the code? Yes, it is, but only at a granular level, and only accessible to those who can read it. It gives you detail, but not the big picture. You need to zoom out to comprehend the why. Here’s where I live: This is the equivalent of source code. Only people who have heard of the Dutch village of Heeze can relate this map to the world. It’s missing the context of larger towns and a country. The next map zooms out only a little, but with the context of the country's fifth-largest city, it’s recognizable to all Dutch inhabitants. The next map should be universal. Even if you can’t point out the Netherlands on a map, you must have heard of London. Good documentation provides a hierarchy of such maps, from global and generally accessible to more detailed, requiring more domain-specific knowledge. At every level, there should be sufficient context about the immediately connecting parts. If there is a handover at all, it’s never of the kind: “Here’s my perfect spec. Good luck, and report back in a month”. It’s the finalization of a formal yet flexible document created iteratively with people from relevant disciplines in an ongoing dialogue throughout the development process. It should be versioned, and tied to the software that it describes. Hence the only logical place is together with the source code repository, at least for specifications that describe a well-defined body of code, a module, or a service. Such specs can rightfully be called the ultimate source of truth about what the code does, and why. Because everybody was involved and invested, everybody understands it, and can (in their own capacity) help create and maintain it. However, keeping versioned specs with your software is no automatic protection against mismatches, when changes to the code don’t reflect the spec and vice versa. Therefore, we make the spec executable, by coupling it to testing code that executes the code that the spec covers and validates the results. It sounds so obvious and attractive. Why isn’t everybody doing it if there’s a world of clarity to be gained? There are two reasons: it’s hard and you don’t always need SBE. We routinely overestimate the importance of the automation part, which puts the onus disproportionally on the developers. It may be a deliverable of the process, but it’s only the collaboration that can make it work. More Art Than Science Writing good specifications is hard, and it’s more art than science. If there ever was a need for clear, unambiguous, SMART writing, executable specifications fit the bill. Not everybody has a talent for it. As a developer with a penchant for writing, I flatter myself that I can write decent spec files on my own. But I shouldn’t – not without at least a good edit from a business analyst. For one, I don’t know when my assumptions are off the mark, and I can’t always avoid technocratic wording from creeping in. A process that I favor and find workable is when a businessperson drafts acceptance criteria which form the input to features and scenarios. Together with a developer, they are refined: adding clarity, and edge cases, and removing duplication and ambiguity. Only then can they be rigorous enough to be turned into executable spec files. Writing executable specifications can be tremendously useful for some projects and a complete waste of time for others. It’s not at all like unit testing in that regard. Some applications are huge but computationally simple. These are the enterprise behemoths with their thousands of endpoints and hundreds of database tables. Their code is full of specialist concepts specific to the world of insurance, banking, or logistics. What makes these programs complex and challenging to grasp is the sheer number of components and the specialist domain they relate to. The math in Fintech isn’t often that challenging. You add, subtract, multiply, and watch out for rounding errors. SBE is a good candidate to make the complexity of all these interfaces and edge cases manageable. Then there’s software with a very simple interface behind which lurks some highly complex logic. Consider a hashing algorithm, or any cryptographic code, that needs to be secure and performant. Test cases are simple. You can tweak the input string, seed, and log rounds, but that’s about it. Obviously, you should test for performance and resource usage. But all that is best handled in a code-based test, not Gherkin. This category of software is the world of libraries and utilities. Their concepts stay within the realm of programming and IT. It relates less directly to concepts in the real world. As a developer, you don’t need a business analyst to explain the why. You can be your own. No wonder so many Open Source projects are of this kind. Gherkin Scenario: discount calculator for loyal customers Given I execute a POST call to /api/customer/12345/orders?recentMonths=6 Then I receive a list of 3 OrderInfo objects And a DiscountRequestV1 message for customer 12345 is put on the queue /discountcalculator [ you get the message ]
TestOps is an emerging approach to software testing that combines the principles of DevOps with testing practices. TestOps aims to improve the efficiency and effectiveness of testing by incorporating it earlier in the software development lifecycle and automating as much of the testing process as possible. TestOps teams typically work in parallel with development teams, focusing on ensuring that testing is integrated throughout the development process. This includes testing early and often, using automation to speed up the testing process, and creating continuous testing and improvement. TestOps also works closely with operations teams to ensure that the software is deployed in a stable and secure environment. TestOps is an approach to software testing that emphasizes collaboration between the testing and operations teams to improve the overall efficiency and quality of the software development and delivery processes. Place of TestOps in Software Development The Need for TestOps Initial Investment Adopting DevOps requires an initial investment of time, resources, and financial investment. This can be a significant barrier to adoption for some organizations, particularly those with limited budgets or resources. Learning Curve DevOps requires a significant cultural shift in the way that teams work together, and it can take time to learn new processes, tools, and techniques. This can be challenging for some organizations, particularly those with entrenched processes and cultures. Security Risks DevOps practices can increase the risk of security vulnerabilities if security measures are not properly integrated into the development process. This can be particularly problematic in industries with strict security requirements, such as finance and healthcare. Automation Dependencies DevOps relies heavily on automation, which can create dependencies on tools and technologies that may be difficult to maintain or update. This can lead to challenges in keeping up with new technologies or changing requirements. Cultural Resistance DevOps requires a collaborative and cross-functional culture, which may be difficult to achieve in organizations with siloed teams or where there is resistance to change. Advantages of the TestOps Continuous Testing TestOps allows continuous testing that enables organizations to detect defects early in the development process. This reduces the cost and effort required to fix defects and ensures that software applications can be delivered with high quality. Improved Quality By integrating testing processes into the DevOps pipeline, TestOps ensures that quality is built into software applications from the outset. This reduces the risk of defects and improves the overall quality of the software. Greater Efficiency TestOps enables the automation of testing processes, which can help organizations reduce the time and effort required to test software applications. This can also reduce the costs associated with testing. Increased Collaboration TestOps promotes collaboration between development and testing teams, which can help identify and resolve issues earlier in the development process. This can lead to faster feedback and better communication between teams. Faster Time-to-Market TestOps allows the automation of testing processes, which reduces the time required to test software applications. This enables organizations to release software applications faster, which can give them a competitive advantage in the marketplace. Scope of TestOps in the Future The scope of TestOps in the future is significant as software development continues to become more complex and fast-paced. TestOps combines software testing with DevOps practices. As a result, it is becoming increasingly important for organizations to implement TestOps to ensure that they can deliver high-quality software applications to market quickly. Some of the trends that are likely to shape the future of TestOps include: Increasing Adoption of Agile and DevOps Methodologies Agile and DevOps methodologies are becoming increasingly popular among organizations that want to deliver software applications faster and more efficiently. TestOps is a natural extension of these methodologies, which are likely to become an essential component of Agile and DevOps practices in the future. Greater Focus on Automation Automation is a critical aspect of TestOps and will likely become even more important in the future. The use of automation tools and techniques can help organizations reduce the time and effort required to test software applications while also improving the accuracy and consistency of testing. The Growing Importance of Cloud Computing Cloud computing is becoming increasingly popular among organizations that want to reduce their IT infrastructure costs and improve scalability. TestOps can be implemented in cloud environments, and they are likely to become even more important as more organizations move their software applications to the cloud. Overall, the scope of TestOps in the future is vast, and it is likely to become an essential component of software development practices in the coming years. Conclusion Is TestOps the future of software testing? Obliviously Yes. With the increasing adoption of Agile and DevOps methodologies, there is a growing need for software testing processes that can keep pace with rapid development and deployment cycles. TestOps can help organizations achieve this by integrating testing into the software development lifecycle and ensuring that testing is a continuous and automated process. Furthermore, as more and more software is deployed in cloud environments, TestOps will become even more important in ensuring that applications are secure, scalable, and reliable. In summary, TestOps is a key trend in software testing that is likely to continue to grow in the future as organizations look for ways to improve the efficiency and quality of their software development and delivery processes.
Senior Software QA Engineer,
Head of software testing department,