SeaLights for Quality Assurance
Automated Test
Prioritization
With a myriad of exponentially growing tests being executed from build to build, running tests in the correct order is imperative. Execute tests based on their importance to fail faster and save time. Tests focusing on high risk areas, recently changed code and, failed test should always be run first.
Identify redundant and ineffective tests and remove them without increasing risk.
Exploratory Test
Code Coverage
Exploratory testing is simultaneous learning, test design and, test execution of critical areas in the application. It is an efficient method to provide rapid feedback and uncover critical bugs in new features. As these tests are manual and their execution duration longer, it’s important to minimize overlap with other test types. Gain visibility into which code areas are being tested with manual and exploratory tests to improve team efficiency and speed.
Functional Test
Code Coverage
Applications are developed with multiple builds each containing unique incremental code changes. In a reality of incremental code changes, especially functional, API and regression testing, CICD pipelines need Functional Test Code Coverage to enable visibility and quick feedback loops.
Measure Functional Test Code Coverage to enable visibility and quick feedback loops.
Test Analytics Reports
Test Analytics provide visibility into your test status across teams, environments and, applications. Deploy automated quality gates to approve or fail builds based on real time data and software quality analytics. Improve your teams release speed and team efficiency with software test analytics and quality gates.
Block Untested Code Changes From Production
Untested code in production causes failures, bad customer experience and time wasted on hotfixes. SeaLights analyzes changes to your code and notifies you whenever an untested code change has slipped through. Stop risky code before it’s shipped and solve critical issues ahead of time.
Test Impact Analysis & Risk Based Testing
Test Impact Analysis (TIA) can be used to speed up the test automation phase of a build. By analyzing the call-graph of the source code it deduces which tests should be run after changes to production code. This method decreases the risk level of the build as the impacted areas will now be tested. Leverage SeaLights to correlate between code areas and relevant tests.
Focus Test
Investment Efforts
Build data driven roadmaps for both automated and exploratory tests that focus on the most critical areas in your application. Invest in areas with many code additions and changes but,low coverage. Leverage a consolidated view of code changes correlated to what your tests are actually testing when put in action.
Optimize Test Execution Duration
With multiple builds, hundreds of changes and, thousand of tests executed with each build, test execution duration is a bottleneck. Gain visibility into what code each test is covering, avoid unneeded overlaps and, reduce test execution duration to the bare minimum needed without raising the risk to your application.
Block Untested Code Changes From Production
Untested code in production causes failures, bad customer experience and time wasted on hotfixes. SeaLights analyzes changes to your code and notifies you whenever an untested code change has slipped through. Stop risky code before it’s shipped and solve critical issues ahead of time.
Automated Test Prioritization
With a myriad of exponentially growing tests being executed from build to build, running tests in the correct order is imperative. Execute tests based on their importance to fail faster and save time. Tests focusing on high risk areas, recently changed code and, failed test should always be run first.
Identify redundant and ineffective tests and remove them without increasing risk.
Automated Test Prioritization
With a myriad of exponentially growing tests being executed from build to build, running tests in the correct order is imperative. Execute tests based on their importance to fail faster and save time. Tests focusing on high risk areas, recently changed code and, failed test should always be run first.
Identify redundant and ineffective tests and remove them without increasing risk.
Exploratory Test Code Coverage
Exploratory testing is simultaneous learning, test design and, test execution of critical areas in the application. It is an efficient method to provide rapid feedback and uncover critical bugs in new features. As these tests are manual and their execution duration longer, it’s important to minimize overlap with other test types. Gain visibility into which code areas are being tested with manual and exploratory tests to improve team efficiency and speed.
Functional Test Code Coverage
Applications are developed with multiple builds each containing unique incremental code changes. In a reality of incremental code changes, especially functional, API and regression testing, CI/CD pipelines need Functional Test Code Coverage to enable visibility and quick feedback loops.
Measure Functional Test Code Coverage to enable visibility and quick feedback loops.
Test Analytics Reports
Test Analytics provide visibility into your test status across teams, environments and, applications. Deploy automated quality gates to approve or fail builds based on real time data and software quality analytics. Improve your teams release speed and team efficiency with software test analytics and quality gates.
Test Impact Analysis & Risk Based Testing
Test Impact Analysis (TIA) can be used to speed up the test automation phase of a build. By analyzing the call-graph of the source code it deduces which tests should be run after changes to production code. This method decreases the risk level of the build as the impacted areas will now be tested. Leverage SeaLights to correlate between code areas and relevant tests.
Focus Test Investment Efforts
Build data driven roadmaps for both automated and exploratory tests that focus on the most critical areas in your application. Invest in areas with many code additions and changes but,low coverage. Leverage a consolidated view of code changes correlated to what your tests are actually testing when put in action.
Optimize Test Execution Duration
With multiple builds, hundreds of changes and, thousand of tests executed with each build, test execution duration is a bottleneck. Gain visibility into what code each test is covering, avoid unneeded overlaps and, reduce test execution duration to the bare minimum needed without raising the risk to your application.
We Support Everything
Angular
Apache Ant
Apache
Ava
Babel
BitBucket
Blaze Meter
C#
Circle CI
Cucumber
GitHub
GitLab
Gradle
Jasmine
Java
Jenkins
Karma
Load Runner
Mocha
NUnit
Protractor
Python
React
Sahi
Soasta
Team City
Telerik
TestNG
TFS
Webpack
We Support Everything
| | | |
|---|---|---|
| Angular | Apache Ant | Apache Maven |
| | | |
| AVA | Babel | Bitbucket |
| | | |
| Blaze Meter | C# | Circle CI |
| | | |
| Cucumber | Github | GitLab |
| | | |
| Gradle | Jasmine | Java |
| | | |
| Jenkins | Karma | Load Runner |
| | | |
| Mocha | NUnit | Protractor |
| | | |
| Python | React | Sahi |
| | | |
| Soasta | Team City | Telerik |
| | | |
| TestNG | TFS | Webpack |
