Test Data Management (TDM) is crucial for ensuring that software applications are tested with realistic, comprehensive data, making it possible to mimic real-world scenarios. Managing and creating this data efficiently is important for successful test execution. Below are the Top 21 Test Data Management Tools, along with their major features, to help streamline your testing processes and ensure data integrity across test environments.

Here’s an outline for your post on Top 21 Test Data Management Tools:

1. Delphix

Delphix is a data virtualization platform that allows for the rapid delivery of test data across the development pipeline. It provides an efficient way to manage large datasets by creating virtualized copies of the data that can be used for testing, reducing the time and resources needed for data provisioning. Delphix also supports data masking to protect sensitive information and can integrate with various databases and cloud environments, making it suitable for complex enterprise environments.

2. Informatica Test Data Management

Informatica Test Data Management (TDM) is a comprehensive solution that allows organizations to manage, create, and maintain test data across different environments. It enables teams to quickly generate high-quality test data while maintaining data privacy through data masking. The tool supports automation, data subsetting, and real-time provisioning, helping teams to accelerate testing cycles while ensuring data consistency and compliance.

3. IBM InfoSphere Optim

IBM InfoSphere Optim is a test data management tool that provides a robust platform for managing test data across multiple environments. It offers capabilities like data masking, data subsetting, and real-time provisioning. InfoSphere Optim allows organizations to streamline their test data processes, ensuring data security and compliance while enabling teams to access the data they need for testing without compromising on quality.

4. Datafaker

Datafaker is an open-source tool for generating synthetic test data. It helps teams create realistic, anonymized data that can be used for functional, integration, and performance testing. Datafaker supports various data types and formats, including dates, addresses, and email addresses, enabling teams to create complex test datasets. It is highly customizable, and teams can use it to generate specific data based on their testing requirements.

5. GenRocket

GenRocket is a test data generation tool that automates the creation of test data across various test environments. It allows teams to generate dynamic and realistic test data on-demand by using a test data generation engine. GenRocket supports a wide range of use cases, including functional, regression, and performance testing. The tool also supports data masking and data encryption, ensuring that sensitive information is protected.

6. Oracle Data Masking and Subsetting

Oracle Data Masking and Subsetting is a tool designed to create secure and compliant test data by masking sensitive information and creating smaller, more manageable subsets of large production datasets. The tool supports various data sources, including Oracle, SQL Server, and MySQL, and can integrate with other Oracle tools like Oracle Enterprise Manager and Oracle Data Integrator. It is particularly useful for organizations in regulated industries that need to maintain data privacy.

7. Talend Data Fabric

Talend Data Fabric is a unified data management platform that provides data integration, data quality, and data governance capabilities. Its test data management functionality includes data masking, data profiling, and data cleansing, helping teams ensure the quality and security of their test data. Talend’s platform supports integration with various databases, cloud platforms, and third-party systems, making it a versatile tool for managing test data in diverse environments.

8. Test Data Manager by CA Technologies

Test Data Manager by CA Technologies is an enterprise-grade solution for managing test data across complex IT environments. It provides capabilities for data subsetting, data masking, and virtualized test data delivery, enabling teams to quickly and securely create test environments with high-quality data. The tool integrates with various CI/CD pipelines and supports automation, making it a great choice for Agile and DevOps teams.

9. Mockaroo

Mockaroo is an easy-to-use online tool for generating realistic test data in a variety of formats. It allows teams to define custom schemas, including names, addresses, dates, and financial data, and generate large datasets for testing purposes. Mockaroo supports JSON, CSV, SQL, and Excel formats, making it a versatile option for generating test data. Its simple interface and API make it ideal for teams looking for a quick and flexible solution for test data generation.

10. DatProf

DatProf is a test data management platform that provides data masking, data subsetting, and data generation capabilities. It helps teams create realistic test data while ensuring data security and compliance. DatProf supports integration with a wide range of databases and applications and provides advanced reporting and analytics features to help teams track and optimize their test data usage.

11. DTM (Data Test Management)

DTM (Data Test Management) is a comprehensive test data management tool that focuses on creating, managing, and maintaining high-quality test data across different test environments. It provides data masking, data subsetting, and test data automation capabilities. DTM also integrates with CI/CD tools, enabling teams to generate test data dynamically as part of the continuous testing process, ensuring efficient test cycles.

12. TestData.io

TestData.io is a cloud-based test data management tool that automates the creation, provisioning, and management of test data. It enables teams to generate synthetic test data that can be used for various testing scenarios, including performance and regression testing. TestData.io supports data masking and can be easily integrated into CI/CD workflows to streamline the process of managing test data in Agile and DevOps environments.

13. Redgate SQL Data Generator

Redgate SQL Data Generator is a powerful tool designed for generating realistic test data for SQL Server databases. It allows users to quickly create data for various types of fields, such as names, email addresses, and dates, and supports a wide range of data formats. The tool also provides the ability to configure custom data generation rules and integrates with SQL Server Management Studio (SSMS), making it an excellent choice for teams working with relational databases.

14. Subsetter by Informatica

Subsetter by Informatica is a tool that allows teams to create smaller, more manageable datasets by selecting relevant subsets from large production databases. It is particularly useful for reducing the size of test datasets without losing critical data. Subsetter supports integration with other Informatica tools and is widely used in complex data environments to streamline the process of data management and provisioning.

15. Data Generator for Salesforce

Data Generator for Salesforce is a test data management tool specifically designed for generating test data for Salesforce applications. It enables teams to create realistic, customized data for Salesforce testing, including accounts, contacts, leads, and opportunities. The tool integrates with Salesforce environments and helps teams automate the generation of test data, ensuring that testing is conducted with accurate and relevant data.

16. Data Management Platform (DMP) by SAP

SAP’s Data Management Platform offers comprehensive features for managing and provisioning test data in large, complex environments. The platform provides capabilities for data masking, data subsetting, and data integration, ensuring that test data is secure, accurate, and aligned with business requirements. SAP DMP integrates well with other SAP solutions and can be scaled to meet the needs of large enterprise environments.

17. Tonic.ai

Tonic.ai is a test data management platform that leverages synthetic data generation to help teams create realistic test data for their applications. The tool uses AI and machine learning algorithms to generate synthetic data that preserves the statistical properties of production data while ensuring privacy. Tonic.ai provides features like data anonymization and integrates with popular testing frameworks and CI/CD tools, making it ideal for modern testing environments.

18. GridGain

GridGain is a test data management solution that offers in-memory data grid capabilities for managing large volumes of test data. It provides fast, real-time access to test data, enabling teams to quickly spin up test environments and provision data for various testing scenarios. GridGain’s distributed architecture makes it suitable for testing applications that need to scale, and its integration with popular testing tools helps streamline the process.

19. TestDataManager

TestDataManager is a test data management tool that allows users to automate the generation, masking, and management of test data. It supports multiple databases and integrates with popular CI/CD pipelines, making it a great choice for DevOps and Agile teams. TestDataManager’s ability to create realistic test data and maintain data privacy through masking features ensures that testing is conducted securely and efficiently.

20. Apex Data Solutions

Apex Data Solutions provides a comprehensive suite of tools for managing and generating test data. It offers data generation, data subsetting, and data masking features that help teams create high-quality test data for their applications. Apex’s platform supports integration with popular databases and automation tools, allowing teams to streamline their testing workflows and ensure that test data is always accurate and up-to-date.

21. Test Data Management by Veritas

Veritas offers a test data management tool that allows teams to create, manage, and provision test data from production systems. It helps organizations reduce the complexity of managing test environments and ensures data consistency and security. The tool supports data masking, subsetting, and automation, making it an excellent choice for teams that need to manage large datasets and comply with data privacy regulations.

This list provides a wide range of test data management tools, each with unique features designed to streamline the process of managing and generating test data. Let me know if you need any additional details or adjustments!

The post Top 21 Tools for Test Data Management Tools in 2026 appeared first on scmGalaxy.

Test management is a critical part of the software development lifecycle, ensuring that testing processes are organized, efficient, and traceable. The following Top 21 Test Management Tools will help streamline your testing efforts, track progress, and maintain the quality of the software through comprehensive management of test cases, plans, and results.

1. TestRail

TestRail is a widely-used test management tool that helps teams organize and track their testing activities. It offers a comprehensive solution for managing test cases, test runs, and reports. With features like customizable workflows, real-time reporting, and integration with popular issue-tracking systems like Jira, TestRail makes it easy for teams to ensure that their tests align with project requirements. It also provides detailed metrics and insights into test execution, helping teams improve their testing process.

2. Quality Center (ALM)

Quality Center (formerly known as HP ALM) is an enterprise-level test management tool that provides a centralized platform for managing the entire test lifecycle. It supports test case design, test execution, and defect management, ensuring that teams can collaborate effectively throughout the testing process. Quality Center also integrates with other HP tools and third-party systems, offering flexibility for large teams and organizations. Its extensive reporting capabilities allow teams to track test progress and measure test effectiveness.

3. Jira with Xray

Jira, combined with Xray, provides a robust test management solution for Agile teams. Xray is a test management plugin for Jira that enables teams to manage both manual and automated tests within the Jira environment. It allows for seamless integration with existing workflows, tracking of test results, and linking of test cases to user stories or requirements. Xray’s reporting and traceability features provide transparency into test execution and coverage, making it a popular choice for Agile test management.

4. PractiTest

PractiTest is a cloud-based test management platform that provides a comprehensive suite of tools for managing tests, defects, and requirements. It allows teams to design and run tests, track defects, and generate detailed reports. PractiTest’s intuitive interface and robust reporting capabilities help teams collaborate efficiently and track test results in real-time. It also integrates with popular CI/CD tools, issue trackers, and automation tools, providing seamless workflows for modern development teams.

5. Zephyr

Zephyr is a leading test management tool that integrates seamlessly with Jira, offering real-time test case creation, execution, and reporting. It enables teams to manage test cycles and track testing progress efficiently. Zephyr supports both manual and automated testing, allowing teams to use it in diverse testing environments. It provides rich reporting and traceability features, which help ensure complete test coverage and highlight areas requiring attention. Zephyr’s integration with Jira allows for easy linking of test cases to project requirements and issues.

6. TestLink

TestLink is an open-source test management tool that provides a comprehensive solution for managing test cases, test plans, and execution. It offers features like test case versioning, requirements tracking, and integration with popular bug tracking tools like Bugzilla, Jira, and Redmine. TestLink’s open-source nature makes it highly customizable, allowing teams to tailor it to their specific testing needs. It also supports automated test execution via integration with tools like Jenkins, making it suitable for both manual and automated testing workflows.

7. TestLodge

TestLodge is a test management tool designed to streamline the test case creation, execution, and tracking process. It provides an easy-to-use interface for managing test plans, organizing test cases, and tracking test results. TestLodge allows teams to integrate with issue trackers like Jira and communication tools like Slack, ensuring smooth collaboration throughout the testing process. With built-in reporting features, TestLodge helps teams quickly analyze test results and share progress with stakeholders.

8. qTest

qTest is a comprehensive test management platform that supports both manual and automated testing. It allows teams to create and manage test cases, plan test cycles, and track defects. qTest offers seamless integration with popular Agile tools like Jira, as well as CI/CD platforms and test automation tools. Its real-time dashboards and reporting capabilities help teams monitor progress, identify bottlenecks, and make data-driven decisions to improve test coverage and quality.

9. TestCollab

TestCollab is a cloud-based test management tool that helps teams organize, execute, and track manual and automated tests. It provides an intuitive interface for creating and managing test cases, executing test runs, and tracking results. TestCollab supports integration with popular bug tracking systems like Jira, as well as automation frameworks like Selenium and Jenkins, making it suitable for teams using Agile and DevOps methodologies. Its detailed reporting features enable teams to measure test effectiveness and continuously improve their processes.

10. IBM Engineering Test Management

IBM Engineering Test Management is an enterprise-grade test management solution designed to manage complex test cycles. It offers end-to-end traceability, from requirements to test execution, ensuring that teams can track and manage all aspects of the testing process. The tool supports both manual and automated testing and integrates with other IBM tools, such as IBM Engineering Requirements Management, and third-party solutions. IBM Engineering Test Management also provides advanced reporting and analytics, allowing teams to track performance metrics and optimize testing workflows.

11. Test Case Studio

Test Case Studio is a test management tool focused on simplifying the creation of test cases and improving collaboration among testing teams. It provides an easy-to-use interface for writing test cases, executing tests, and tracking results. Test Case Studio allows users to create reusable test case templates, improving consistency across tests. It integrates with popular issue trackers and provides features like version control and real-time reporting for tracking progress.

12. Helix ALM

Helix ALM (formerly TestTrack) is a comprehensive test management tool designed to manage the full lifecycle of test cases and defects. It allows teams to create test plans, manage test execution, and track test results. Helix ALM offers strong traceability features, ensuring that teams can trace requirements to test cases and test results. The tool also integrates with various development and CI/CD tools, streamlining the testing process and enabling efficient collaboration across teams.

13. Test Case Management (TCM)

Test Case Management (TCM) is a test management tool designed to help teams organize and track test cases, execution, and defects. It offers a user-friendly interface for managing test cases, creating test runs, and logging defects. TCM provides built-in reporting and analytics, allowing teams to monitor testing progress and identify areas for improvement. It integrates with other issue tracking tools like Jira and supports both manual and automated testing workflows.

14. Xray for Jira

Xray for Jira is a test management plugin for Jira that provides comprehensive support for both manual and automated testing. It enables teams to create and manage test cases, execute tests, and track results directly within the Jira environment. Xray integrates with other CI/CD tools like Jenkins, as well as test automation frameworks like Cucumber and JUnit, making it a powerful tool for Agile teams. With real-time reporting and traceability, Xray ensures that teams maintain full visibility of test progress and coverage.

15. Allure Testops

Allure Testops is a test management and reporting tool that integrates with test automation frameworks to provide detailed insights into test results. It allows teams to manage test cases, track execution, and generate custom reports. Allure Testops supports integration with popular CI/CD tools like Jenkins, as well as test automation frameworks like Selenium and Appium. Its real-time reporting and analytics capabilities enable teams to identify issues quickly and optimize their testing process.

16. PractiTest

PractiTest is a cloud-based test management tool that helps teams manage their testing process from test case creation to defect tracking. It provides an intuitive interface for managing tests, tracking results, and generating reports. PractiTest integrates with popular bug tracking tools like Jira, as well as automation frameworks like Selenium and Jenkins. It also offers robust reporting and analytics capabilities, helping teams make data-driven decisions to improve testing efficiency.

17. Mabl

Mabl is an intelligent test automation platform that also includes test management capabilities. It allows teams to create, execute, and manage automated tests for web applications. Mabl uses AI to enhance test creation and execution, making it easier for teams to identify issues and improve test coverage. The platform offers real-time insights into test results and integrates with popular CI/CD tools, enabling seamless workflows for continuous testing and delivery.

18. Katalon TestOps

Katalon TestOps is a comprehensive test management solution designed for managing automated tests. It provides tools for test case management, execution, and reporting, making it easy for teams to track test results and identify issues. Katalon TestOps integrates with popular CI/CD tools and test automation frameworks like Katalon Studio, Selenium, and Appium. It offers detailed reporting and analytics, allowing teams to monitor test progress and improve test coverage.

19. Testlink

Testlink is an open-source test management tool that provides a platform for creating and managing test cases, executing tests, and tracking results. It supports integration with various bug tracking tools and test automation frameworks. Testlink is ideal for teams looking for a customizable solution that can be tailored to their specific test management needs. It also offers detailed reporting and versioning capabilities, making it easy to manage test cycles and track progress.

20. Agile Central (formerly Rally)

Agile Central is a comprehensive Agile project management tool that includes test management functionality. It helps teams manage their test cases, track defects, and integrate testing with the Agile development process. Agile Central provides strong reporting and analytics capabilities, helping teams measure the success of their testing efforts and identify bottlenecks. It integrates with other tools like Jira and Jenkins, making it suitable for teams following Agile and DevOps methodologies.

21. Testrail

Testrail is a popular test management tool that provides a comprehensive platform for managing test cases, test runs, and reporting. It offers seamless integration with issue trackers like Jira and CI/CD tools like Jenkins, making it easy to track testing progress and automate workflows. Testrail’s real-time reporting and customizable dashboards provide insights into test execution, ensuring that teams can stay on top of their testing efforts and deliver high-quality software.

This list provides a range of tools that offer comprehensive test management capabilities. Let me know if you’d like further details on any of these tools!

The post Top 21 Tools for Test Management Tools in 2026 appeared first on scmGalaxy.

The post Test Strategy appeared first on scmGalaxy.

This is a test 2

Regards,
Rajesh Kumar
Twitt me @ twitter.com/RajeshKumarIn

The post This is a test 2 appeared first on scmGalaxy.

How to get the results with graphics for each action in each build in Jenkins

Ex: checkout code from Git -> 1 min
Build -> 3 mins
unittest -> 15mins (why it is taking 15 mins)
integration test -> 3 mins
Deployment -> 2 mins (2 different locations)
Emails -> 1 mins

Display the test results for each test cases in Unit test and Integration
test also.

The post Test Results appeared first on scmGalaxy.

1. Write a cookbook using you can install apache http server in CentOs and Ubuntu, enable the services and start the service.
2. Write a recipe using template resources to create a file, but only if an attribute has a specific value.
   1. Write a recipe to create a file using a string, but not if “/etc/passwd” exists?
3. Write a cookbook to unzip a file, and then move a files from one location to another using batch, bash chef resources?
4. Create a role and assign any two cookbooks to role and setup a 2 nodes assigned to role and conerse the role.
5. Write a cookbook using install version of latest tomcat and deploy jenkins.war files into war.
6. Write a 4 cookbooks php, mysql, apache and webapp respectively and map a dependency between them and install a sample web application.
7. Write a cookbook to install git, wget, zip in RHEL and Ubuntu.
8. Write a cookbook in which create a file using a string, but only if “/etc/passwd” exists
9. Write a coobook to run a batch file that unzips and then moves files from one location to another.

The post Write 10 Chef Cookbooks to test your chef programming skills appeared first on scmGalaxy.

1. Write a cookbook using you can install apache http server in CentOs and Ubuntu, enable the services and start the service.

2. Write a recipe using template resources to create a file, but only if an attribute has a specific value.

3. Write a recipe to create a file using a string, but not if “/etc/passwd” exists?

4. Write a cookbook to unzip a file, and then move a files from one location to another using batch, bash chef resources?

5. Create a role and assign any two cookbooks to role and setup a 2 nodes assigned to role and conerse the role.

6. Write a cookbook using install version of latest tomcat and deploy jenkins.war files into war.

7. Write a 4 cookbooks php, mysql, apache and webapp respectively and map a dependency between them and install a sample web application.

8. Write a cookbook to install git, wget, zip in RHEL and Ubuntu.

9. Write a cookbook in which create a file using a string, but only if “/etc/passwd” exists

10. Write a coobook to run a batch file that unzips and then moves files from one location to another.

The post Write 10 Chef Cookbooks to test your chef programming skills appeared first on scmGalaxy.

The post SQL Injection: How to check or test for vulnerabilities appeared first on scmGalaxy.

There is not any official distinguished between code Coverage and Test Coverage. Some practitioner has expressed their difference opinion in terms of defining Code Coverage and Test Coverage.
Code coverage and test coverage metrics are both measurements that can be seful to assess the quality of your application code. Code coverage is a term to describe which application code is exercised when the application is running.

Whereas Test coverage refers to metrics in an overall test-plan. In this expert  response, you’ll learn how quality assurance professionals use both of these metrics effectively.

Another definition found over the google search as below;
Code coverage is a measure of how much code is executed during testing &
Test coverage is a measure of how many test cases have been executed during testing.

Lets know about  Code Coverage by definition more in details.
In computer science, code coverage is a measure used to describe the degree to which the source code of a program is tested by a particular test suite. A program with high code coverage has been more thoroughly tested and has a lower chance of containing software bugs than a program with low code coverage. Many different metrics can be used to calculate code coverage; some of the most basic are the percent of program subroutines and the percent of program statements called during execution of the test suite.

Basic coverage criteria

There are a number of coverage criteria, the main ones being:

• Function coverage – Has each function (or subroutine) in the program been called?
• Statement coverage – Has each statement in the program been executed?
• Branch coverage – Has each branch (also called DD-path) of each control structure (such as in if and case statements) been executed? For example, given an if statement, have both the true and false branches been
• executed? Another way of saying this is, has every edge in the program been executed?
• Condition coverage (or predicate coverage) – Has each Boolean sub-expression evaluated both to true and false?

[Taken from Wikipedia]

Simply put, code coverage is a way of ensuring that your tests are actually testing your code. When you run your tests you are presumably checking that you are getting the expected results. Code coverage will tell you how much of your code you exercised by running the test. Your tests may all pass with flying colours, but if you’ve only tested 50% of your code, how much confidence can you have in it?

The post Difference between Code Coverage and Test Coverage | Code Coverage VS Test Coverage appeared first on scmGalaxy.

When writing JUnit tests developers often add log statements that can help provide information on test failures. During the initial attempt to find a failure a simple System.out.println() statement is usually the first resort of most developers.

Replacing these System.out.println() statements with log statements is the first improvement on this technique. Using SLF4J (Simple Logging Facade for Java) provides some neat improvements using parameterized messages. Combining SLF4J with JUnit 4 rule implementations can provide more efficient test class logging techniques.

Some examples will help to illustrate how SLF4J and JUnit 4 rule implementation offers improved test logging techniques. As mentioned the inital solution by developers is to use System.out.println() statements. The simple example code below shows this method.

01    import org.junit.Test;
02
03    public class LoggingTest {
04
05      @Test
06      public void testA() {
07        System.out.println(“testA being run…”);
08      }
09
10      @Test
11      public void testB() {
12        System.out.println(“testB being run…”);
13      }
14    }

The obvious improvement here is to use logging statements rather than the System.out.println() statements. Using SLF4J enables us to do this simply whilst allowing the end user to plug in their desired logging framework at deployment time. Replacing the System.out.println() statements with SLF4J log statements directly results in the following code.
view source

01    import org.junit.Test;
02    import org.slf4j.Logger;
03    import org.slf4j.LoggerFactory;
04
05    public class LoggingTest {
06
07      final Logger logger =
08        LoggerFactory.getLogger(LoggingTest.class);
09
10      @Test
11      public void testA() {
12        logger.info(“testA being run…”);
13      }
14
15      @Test
16      public void testB() {
17        logger.info(“testB being run…”);
18      }
19    }

Looking at the code it feels that the hard coded method name in the log statements would be better obtained using the @Rule TestName JUnit 4 class. This Rule makes the test name available inside method blocks. Replacing the hard coded string value with the TestName rule implementation results in the following updated code.

01    import org.junit.Rule;
02    import org.junit.Test;
03    import org.junit.rules.TestName;
04    import org.slf4j.Logger;
05    import org.slf4j.LoggerFactory;
06
07    public class LoggingTest {
08
09      @Rule public TestName name = new TestName();
10
11      final Logger logger =
12        LoggerFactory.getLogger(LoggingTest.class);
13
14      @Test
15      public void testA() {
16        logger.info(name.getMethodName() + ” being run…”);
17      }
18
19      @Test
20      public void testB() {
21        logger.info(name.getMethodName() + ” being run…”);
22      }
23    }

SLF4J offers an improved method to the log statement in the example above which provides faster logging. Use of parameterized messages enable SLF4J to evaluate whether or not to log the message at all. The message parameters will only be resolved if the message will be logged. According to the SLF4J manual this can provide an improvement of a factor of at least 30, in case of a disabled logging statement.

Updating the code to use SLF4J parameterized messages results in the following code.

01    import org.junit.Rule;
02    import org.junit.Test;
03    import org.junit.rules.TestName;
04    import org.slf4j.Logger;
05    import org.slf4j.LoggerFactory;
06
07    public class LoggingTest {
08
09      @Rule public TestName name = new TestName();
10
11      final Logger logger =
12        LoggerFactory.getLogger(LoggingTest.class);
13
14      @Test
15      public void testA() {
16        logger.info(“{} being run…”, name.getMethodName());
17      }
18
19      @Test
20      public void testB() {
21        logger.info(“{} being run…”, name.getMethodName());  }
22      }
23    }

Quite clearly the logging statements in this code don’t conform to the DRY principle.

Another JUnit 4 Rule implementation enables us to correct this issue. Using the TestWatchman we are able to create an implementation that overrides the starting(FrameworkMethod method) to provide the same functionality whilst maintaining the DRY principle. The TestWatchman Rule also enables developers to override methods invoked when the test finishes, fails or succeeds.

Using the TestWatchman Rule results in the following code.

01    import org.junit.Rule;
02    import org.junit.Test;
03    import org.junit.rules.MethodRule;
04    import org.junit.rules.TestWatchman;
05    import org.junit.runners.model.FrameworkMethod;
06    import org.slf4j.Logger;
07    import org.slf4j.LoggerFactory;
08
09    public class LoggingTest {
10
11      @Rule public MethodRule watchman = new TestWatchman() {
12        public void starting(FrameworkMethod method) {
13          logger.info(“{} being run…”, method.getName());
14        }
15      };
16
17      final Logger logger =
18        LoggerFactory.getLogger(LoggingTest.class);
19
20      @Test
21      public void testA() {
22
23      }
24
25      @Test
26      public void testB() {
27
28      }
29    }

And there you have it. A nice test code logging technique using JUnit 4 rules taking advantage of SLF4J parameterized messages.

I would be interested to hear from anyone using this or similar techniques based on JUnit 4 rules and SLF4J.

Reference: http://www.catosplace.net/

The post JUnit 4 Test Logging Tips using SLF4J appeared first on scmGalaxy.