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9 Reasons why Requirements traceability is important in Agile

Requirements traceability is the ability to describe and follow the life of a business/technical requirement in both, forward and backward, directions (i.e., from its origins, through its development and specification, to its subsequent deployment and use, and through periods of ongoing refinement and iteration in any of these phases) – Source Wikipedia

Requirements traceability provides a context to the development team and sets expectations & goals for the testing team.

Requirements tracing is bi-directional.

Forward tracing: Requirements are traced forward to test cases, test execution, and test results

Back tracing: Features/functions are traced back to their source, typically a business requirement

What is the need for Requirements traceability?

To ensure compliance :All specified requirements have been met and final deliverables can be traced back to a business need. It also makes sure that every requirement is accounted for.

Helpful in analysis and design :Requirements traceability ensures that each business need has been translated to a requirement, which further transforms into technical specifications, and then resulting in a deliverable.

Change impact analysis : In case of any requirement changes, traceability helps in finding the affected workflow, design, and impacted test cases.

Ensures comprehensive test coverage :Requirement traceability ensures that the right test cases, which are mapped to the requirement under test, are executed. This further improves the test coverage since all the test cases are accounted for.

Helps in optimizing the testing process : Not all test cases that have been generated need to be executed in case there are any changes. Traceability aids in identifying impacted parts and relevant test cases can then be executed, thus resulting in an optimal test process.

Aids in keeping track of project progress  :Analysis of the requirements and the test results, and tracing it back to origins, helps in keeping track of how far the project has progressed. It also helps in keeping tabs on reality check whether the timelines will be practically met or not.

Decision making and analysis :Any changes in the business need have a direct impact on requirements, which further propagates to the development and testing process. Requirement traceability helps in isolating the impacted requirements, thus identifying and updating the test cases associated with them. It makes it easier for the teams to analyze the impact of the change across the board.

Faster time to market :Requirements traceability accelerates the overall development process, thus speeding up the release cycles. The number of defects is inversely proportional to requirements traceability. As the traceability increases, the defect rate goes down, simply because it becomes easier for the development team to understand and relate, and for the testing team to understand the context and scope of testing.

Increased customer confidence : Requirements traceability helps in gaining and maintaining customer’s trust and confidence by increased transparency. Customers can be assured in the knowledge that all their business needs have been accounted for and are traceable to a fully functional and high-quality end product

What is a typical Requirements traceability life cycle?

What is the Requirements Traceability Matrix?

Requirement traceability matrix tools is a testing artifact that keeps track of all the user requirements and the details of the test cases mapped to each of those requirements. It serves as a documented proof that all the requirements have been accounted for and validated to achieve their end purpose.

Contents of a typical RTM are

Requirements Traceability in DevOps and its challenges

It is quite evident from the previous sections that requirements tracing has a plethora of benefits, but does it work perfectly in the DevOps scenario?

In agile environments, keeping track of the requirements for every sprint and release can be a herculean task since the requirements keep evolving. One may tend to think whether all these efforts are worth it, without compromising on the release timeline and overall quality.

Two common challenges faced while striving for end-to-end requirements traceability are collaboration and maintenance.

Collaboration: Many cross-functional teams are involved when it comes to development and testing in DevOps. It calls for better collaboration for ensuring that all the requirements are effectively realized, without compromising on quality. Requirements keep evolving with every sprint and keeping traceability up to date in synchronization with various teams is a daunting task.

Maintenance: With the frequent changes of software artifacts in a DevOps environment, managing traceability for those artifacts consistently throughout the software development is extremely difficult. Therefore, traceability maintenance to cope with the continuous artifact changes becomes tough. The next section will explore how the benefits of traceability can be integrated into DevOps to handle the above-mentioned issues in order to maximize the benefits.

Achieving Requirements tracing and validation intelligently with Webomates

Faster time to market a quality product is the essence of DevOps, hence it calls for adopting an approach that does not create testing bottlenecks in order to incorporate traceability.

Automation and Shift-Left approach are two pillars of many which render support to DevOps setup. Both of these can also be leveraged upon to incorporate traceability effectively, at the same time addressing the challenge of collaboration and traceability maintenance.

Automation: In case of the projects that are large in size, keeping track of requirements and their realization is a colossal task. That’s when automation helps by having an integrated requirements traceability and management system for end-to-end tracking.

Continuous testing and Continuous integration call for automated traceability checks. Every time, there is a change, the concerned stakeholders should be notified about it so that the change can be traced to the requirement and appropriate action can be taken.

Automation also helps in

Change and version management of requirements

Status tracking of requirements

Shift-Left Approach: It is a good idea to introduce traceability as early as possible, instead of waiting for testing results. The developer and the designing team should be notified on a priority basis, as soon as a code check-in triggers the continuous integration process.

Organizations can reap maximum benefits from traceability by integrating it within existing tools to leverage the power of automated checks.

Webomates provides services to this effect. It addresses the following areas:

Comprehensive test case coverage for an epic/story

Is coverage level suitable as per the complexity of requirement under test?

Audits the coverage level for new stories

Keeps track of most affected features: either due to requirement changes or defect rectifications.

Analyzes the reasons for defects

Checks why a requirement/feature is breaking – Is it due to outdated test cases?

Our tool provides a continuous feedback loop which ensures that requirement changes are traced at every level and updated accordingly.

If this has piqued your interest and you want to know more, then please click here and schedule a demo, or reach out to us at info@webomates.com. We have more exciting articles coming up in the next few days about requirement traceability. Stay tuned and like/follow us Webomates or Aseem.

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Can Artificial Intelligence replace human in Software Testing?

Artificial intelligence is a computer science discipline that simulates intelligence in machines, by making them think, act and mimic human actions. There has been a significant development in the artificial intelligence industries, machines are automated to take rational actions and exhibit traits that are associated with humans. As days go by more and more algorithms are being created to mimic human intelligence and are embedded into machines. Software testing and development is a very important aspect where artificial intelligence is applied. With Digitalization improving human efficiency, so has improvements in AI shaped the way software is being tested.

2016-2017 Quality assurance report suggests that AI will help shape software testing by assisting humans in eliminating problems associated with QA and software testing challenges. However, if these needs are met in the software industries there is a possibility that human testers will become extinct. This calls for the question “Can Artificial intelligence replace humans in software testing?” Many software experts believe that artificial intelligence can only assist in software testing and cannot replace humans, because humans are still needed to think outside the box and explore inherent vulnerabilities in the software. Contrary to this, others think otherwise. But after critical thought and weighing both views, it appears that the former obviously holds more tangible points than the latter.

The Evolution in Software testing is continuous with the adoption of Agile and DevOps methodologies. And software development will also continue to evolve in the era of AI. Artificial intelligence is charged with creating software to understand input data versus output data. This is similar to software tests carried out by human software testers, where the tester types in an input and looks for an expected output. Today, testing tools have evolved. Automation tools can be used to create, organize and prioritize test cases. Efficiently managing tests and their outcomes remain essential to giving the developers the feedback they need.

Shortcomings of Humans in Software testing that can be positively transformed by Artificial intelligence

Although humans are considered a reliable source for software testing, humans still have its own shortcomings. This is a disadvantage to human software testers which reduces their efficiency and performance in software testing. These shortcomings are stated as follows:

Time-consuming: The primary disadvantage of performing software testing by humans is that it is time-consuming. Validation of the functionalities of software might take days and weeks, and with the assistance of Artificial intelligence time wastage is reduced to minimal.

Limited possibilities of testing for manual scenarios: Artificial intelligence creates a broad scope for testing contrary to the limited scope available to the human testing scenarios.

Lack of automation: Manual testing requires the presence of the software tester, but testing using artificial intelligence can be done steadily without much human intervention.

In a large organization without the help of Artificial intelligence automated tools in software testing, there will be low productivity.

Manual testing is not always 100% accurate as it can be exposed to certain errors which may elude the software tester. Some glitches in the software are usually not recognized by the software tester, validation only occurs in certain areas and others are ignored. With AI coverage as well as accuracy can be improved.

Scalability issues: manual testing is a linear process and happens sequential manner. this means that only one test can be created and done at the same time, trying to create more test from other functionalities simultaneously can increase complexity.

You may like to read

Importance of Artificial Intelligence Software Testing Tool

Black – Box Testing Explained

What are the advantages and disadvantages of Artificial intelligence testing tools in software testing?

Artificial intelligence testing tools can work side by side with the software testers in order to achieve improved quality in software testing. Modern Applications interacts with each other through a myriad of APIs which constantly grows in complexity exponentially as technology evolve. Software development life cycle is becoming more complicated by day and thus, management of delivery time is still significant. Therefore, software testers need to work smarter and not harder in this new age of software development. Artificial intelligence testing tools have helped to make software releases and updates that happens once a month to occur on weekly or daily basis. An artificial intelligence testing platform can perform tests more efficiently than human beings, and with constant updates to its algorithm, even the slightest change can be observed in the software. But as much as artificial intelligence has positive achievements in software testing industries, it still has its corresponding disadvantages. Some of these disadvantages are reasons why human contributions cannot be neglected.

Advantages:

Improved accuracy and efficiency: Even the most experienced software testers are bound to make mistakes. Due to how monotonous software testing is, errors are inevitable. This is where artificial intelligence tools help by performing the same test steps accurately each time they are executed and at the same time provides detailed results and feedbacks. Testers are freed from monotonous manual tests giving them more time to explore the application & able to give input for improvements or usability areas.

Increases the Overall test coverage: Artificial intelligence testing tools can help increase the scope of tests, this results in overall improvement of software quality. AI testing tools canscan through the memory, file contents, and data tables in order to determine if the software is behaving as it is expected to and at the same time can provide more triage information or even a root cause..

Saved time + Money = Faster delivery to Market: Due to repetitions that exist in software testing every time a new product is created or modified, a human tester is needed to solve the problem associated with each test case by creating and automating tests. This helps to solve the problem of repetition, thereby saving time and money and help achieve faster delivery. By integrating AI software testing, the overall timespan can be reduced which translates directly into cost savings.

Going beyond the limitations of manual testing: AI testing tools or bots can automatically create tens, hundreds or thousands of virtual set of users that can interact with a network, software or web-based application. This helps software testers to execute a controlled web testing with hundreds of users thereby breaking the limitation of manual testing.

Helps both developers and testers

Disadvantages:

Artificial intelligence in software testing use the concept of GIGO (Garbage in Garbage Out): Most people are looking at artificial intelligent to fix all their testing ills. They hope Artificial intelligence will solve all the problems that manual testing has not been able to address. The simple fact is that a problem that cannot be solved manually cannot be solved by an AI tool as well. AI tools can only solve problems that have been solved manually and has been directed for them to solve digitally. Therefore, an Artificial intelligence tool can only do what it is told to do and cannot go beyond that.

High costs: the high cost associated with acquiring AI tools coupled with cost needed to get it updated with time to meet the latest requirements, can make it inaccessible to individual testers or smaller organizations.

Can’t think outside the box: Automated software testers can only do what they are programmed to do. Their capability is limited and cannot go beyond whatever algorithm or programming is stored in their internal circuit.

Unemployment: Much human software testers won’t be needed to perform software testing Jobs because most of the positions have been occupied by automation tools. Thus, limited positions will now be available for human testers to occupy.

Areas, where artificial intelligence can assist and dominate in software testing, include the following:

Generating test case scenarios

Generating automation script

Predicting the defects as per code changes

Reducing the automation false failure

Self healing of automation script

and many more to come….

In conclusion, assistance of artificial intelligence in software testing is significant and has helped achieve tremendous results in software testing, but more emphasis is laid on inevitable human contributions. With every passing day, as artificial intelligence finds its way into Software testing and other quality assurance fields, organizations are contemplating whether it should be adopted wholly within their quality assurance departments. But evidently, Quality assurance cannot do without human contributions. The salient benefits of AI in software testing are these: it goes beyond the limitations of manual testing, it helps achieve improved accuracy and efficiency in bug and algorithm testing, and it saves time and money involved in software development. It is evident that in the long run, AI will not only be confined to helping software testers, but will be applicable to all roles across software development to deliver top quality software to the market. So answering the subject matter question we can see that the extinction and replacement of Humans by AI is fallacious.

Webomates offers a regression testing service, CQ, that uses manual testing (test case- and exploratory-based), automation,  crowdsourcing with Artificial Intelligence to not only guarantee Full regression in under 24 Hours but also to provide traiged defects. Our customer spend only 1 hour a week on a Full Regression of their software.

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo or reach out to us at info@webomates.com.

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“Code coverage is software metrics for the number of lines, branches, and conditions of code exercised during the execution of your test suite.”

There are multiple criteria used by developers and test engineers to ascertain code coverage.

Read more about it on the blog by Mark Sawers - https://www.webomates.com/blog/automation-testing/get-code-coverage-easily/

Achieve Effortless Code Coverage with Webomates. Schedule a demo of Webomates CQ now at - https://www.webomates.com/schedule-demo/

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Code coverage example in software testing

Webomates Code coverage is a software testing metric that determines the number of lines of code that is successfully validated under a test procedure, which in turn, helps in analysing how comprehensively a software is verified. Webomates Code coverage tool will help you to measure and compare attributes of your test process for better performance and maintainability. Click to know more about this article :  Code coverage example in software testing

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Webomates CQ makes adding system tests to the CI/CD tool chain effortless. The platform can be invoked via an API and the results are posted back into your CI/CD system.

Know more about the solution at - https://lnkd.in/g62dWH93

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Code coverage in software testing

Code coverage is a software testing metric that is used to determine how much code of an application was exercised by the test case. This metric helps in determining how well your tests cover the local code, and also helps in identifying areas that need more code coverage.

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Continuous testing tools in DevOps

A continuous testing tool in DevOps has become a must for any project. Webomates provides a complete suite of continuous testing tools, from fault injection and functional coverage analysis to cloud hosted infrastructure. Click here to learn more about the services offered by Webomates today!

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Test failure analysis is a good quality control measure that provides you with insights on what exactly went wrong, at what point, and understanding the reason behind it.

This helps the teams in improving the testing strategy based on the findings by identifying whether the problem was a testing issue or a flaw in design and development.

Read more at - https://www.webomates.com/blog/artificial-intelligence/test-failure-analysis-with-ai-critical-to-devops/

Schedule a demo of Webomates CQ (Webomates’ testing as a service platform based on intelligent test automation) - https://www.webomates.com/schedule-demo/

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Are your developers writing unit tests that result in code coverage reports?

Let's talk about its possible disadvantages - ➡️ Developers are not writing any new feature ➡️ Developers are spending a significant amount of time using unit tests to perform unit tests and then integration tests and system test ➡️ In order to meet targets, there is an exponential increase in effort for higher levels of code coverage numbers

With Webomates advanced code coverage feature reduce the development effort and increase the code coverage to 80%

Schedule a demo now at - https://www.webomates.com/schedule-demo/

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Migrate in weeks & not months!

Webomates CQ provides Effortless Cloud Migrations for SAP with Continuous Quality.

Know more at - https://www.webomates.com/sap/

Webomates CQ is an ideal platform to scale up and ramp down rapidly in a “Lift and Shift” environment where there is a known predictable need to burst up capacity and tail down upon completion of migration or spike in testing.

Schedule a demo now at - https://www.webomates.com/schedule-demo/

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What is your Biggest Software Challenge ?

➤ Too Many Bugs

Defect Leakage is bad! Read more at - https://www.webomates.com/too-many-bugs/

➤ Getting features to market faster

We are living in a new era where software releases move much faster with customers looking for features weekly if not daily.

Know more at - https://www.webomates.com/getting-features-to-market-faster/

➤ Automation is always out of date

The cost of software test automation is only 20% in the setup phase and the cost of maintenance of the software test automation is 80% of the overall cost.

Read more at - https://www.webomates.com/automation-always-out-of-date/

Webomates CQ, a patented cloud-based testing platform powered by AI technology makes software testing effortless and helps organization overcome all the challenges

Webomates CQ Overview - https://www.webomates.com/overview/

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Automation and AI Automation are fundamentally different techniques.

➤ Test Automation essentially means performing repetitive testing tasks based on the test scripts.

➤ AI test automation involves the software to understand, learn, decide and perform on the basis of these learnings

Read more at - https://www.webomates.com/.../automation-vs-ai-automation/

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Audio testing is an integral part of media testing. Audio quality testing involves checking the application objectively, as well as subjectively.

Know more about it at - https://www.webomates.com/blog/media-testing/guide-to-why-audio-testing-is-important/

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Software Testing Life Cycle

Software Testing is a mandatory part of any Software Development process. It ensures that all technical and business requirements are met satisfactorily. Software Testing Life Cycle (STLC) is a series of systematically planned phases in case of a waterfall software development approach and continuous iterative & agile in case of agile or iterative development of software.

Software testing life cycle comprises of various activities with specific goals. Organizations may tweak these to align with their corporate philosophy, but the basic essence remains the same.

In the waterfall model, the activities are performed in  phases as shown in Waterfall diagram below, whereas in Agile model, all these activities are performed in every release, as shown in the following agile diagram below.

Feature understanding and Test script updates are simultaneous activities which run in every release however, there are activities which are required only once like test environment setup and only new release deployment is required.

Lets understand how testing is carried out in the traditional waterfall approach. To know more about agile testing click here.

Requirement Analysis

The QA team interacts with various stakeholders to understand their requirements for testability. The requirements can be either functional or non-functional in nature.

Priorities are attached to the requirements for testing.

The test conditions are defined in this phase. Every test condition should be traceable to a requirement. To aid this, a Requirement traceability Matrix is maintained where each requirement is mapped with test conditions. Requirement traceability Matrix helps in keeping track of testing.

Testing environment is identified during this phase.

Test Planning and Test Case Development

Test planning phase is sometimes referred to as Test Strategy phase. It is very important from technical and business point of view.

A detailed test plan is created in this phase.

All testing strategies and approaches are defined.

Risk Analysis, Risk management and mitigation strategies are defined.

Scheduling is done for various testing phases.

Once test planning is completed, the team starts working on test cases based on inputs from planning phase.

Detailed Test case Document is prepared.

Test Scripts are prepared for tests marked for automation testing.

Test cases and test scripts are reviewed by peers and managers to ensure complete coverage.

Test data is prepared in test environment. Click to read more about this blog : Software Testing Life Cycle

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo or reach out to us at info@webomates.com

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Test case testing vs Exploratory Testing

So which one is better? Easy answer. You need to do both types of testing. No this is not a case of just doing more is better!! Exploratory testing and test-case based testing take different and complementary approaches for testing a product. Let’s start with basic definitions.

Test case-based testing

In this approach the test team sits down and defines the test cases that they will be carrying out on the day of regression. Here is the definition from softwaretestingfundamentals.com

“A test case is a set of conditions or variables under which a tester will determine whether a system under test satisfies requirements or works correctly.”

Typically the test case will have a series of steps with or without data being entered defined with one or more validation steps carried out to determine that the behavior of the system is occurring correctly.

Exploratory testing

In Exploratory testing the team sits down prior to testing of the next build and creates a charter or a set of guidelines on where to explore. This can include high defect areas, new feature areas, areas where defects are fixed etc. Here is a more formal definition from ISTQB

“Exploratory testing is a hands-on approach in which testers are involved in minimum planning and maximum test execution. The planning involves the creation of a test charter, a short declaration of the scope of a short (1 to 2 hour) time-boxed test effort, the objectives and possible approaches to be used.”

In addition if you are looking for an excellent guide on how to perform Exploratory testing Elisabeth Hendrickson’s book Explore It!! is an easy yet phenomenal read. Click to read more about this blog : Test case testing vs Exploratory Testing

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Test Failure Analysis With AI-Critical To DevOps

Software testing is an extremely important process to ensure that all quality requirements are met before any application is released in the market. With DevOps, the release cycles have become shorter, but expectations are still the same, i.e. high-quality end product. But, it is a fact that the tests fail sometimes and testing may hit a roadblock. Usually, a knee-jerk reaction is to pass the buck to the development team for fixing the issue. However, the ideal approach is to understand the root cause of the failure.

Test failure analysis is a systematic process to analyze and identify the underlying causes for a failed test and to prevent them from occurring again.

Test failure analysis is an important exercise that should be conducted for all critical test failures. Continuous testing generates a lot of data in terms of test results which can be utilized for test failure analysis which can help in sifting, identifying the cause, and fixing the issue. It helps in improving the overall QA Ops process by helping in managing and addressing the root causes of any issue.

Key elements of Test failure analysis

Failure in testing  analysis is a good quality control measure that provides you with insights on what exactly went wrong, at what point, and understand the reason behind it. This helps the teams in improving the testing strategy based on the findings by identifying whether the problem was a testing issue or a flaw in design and development.

Test failure analysis is tailored as per the organization’s QA process and the application under test. However, certain key elements are common across the board and are mentioned below.

📷Root cause analysis

Root cause analysis of the defect holds the key to understanding what exactly went wrong.

Was it because of issues with the software or there was a problem with the test itself?

Once this basic question is answered, it is time to trace the origin of the problem. The problem could be at any point in the whole development cycle: requirement gathering and understanding, or design and development, or in some cases environmental issues while testing. Once the origin is verified, the teams need to work on it for corrective action.

If there is an issue with the test, then the whole test plan needs to be re-examined and corrected before further testing can take place.

Addressing false failures

False failures are the bane of test automation. These are the cases when the automation system should have been able to correctly identify whether it is a Pass or Fail. Instead, it is incorrectly specified as a Fail. We have covered this in detail in our blog, click here to read more. False failures can lead to unnecessary delays because every failed test case  needs to be triaged and based on its priority needs to be addressed.

Detailed Reporting

Good reporting aids the teams to

Understand test results

Saves time in going through a huge amount of data that was generated while testing

Helps in distinguishing between actual defects, script errors, feature change and the noise.

Helps in addressing the key question of how many known defects are in the build, and can the shipping be delayed, while the test team works on classifying the automation failures as actual bugs or a re-test scenario.

Addressing the root cause

Once the actual defects have been identified and traced to their origins, the concerned team needs to first segregate the ones that need immediate attention and address them as per the priorities. Depending on the magnitude of the change, the re-test can be done within the same test cycle or a new sprint can be conducted just for testing.

Acing the test failure analysis with Webomates

In a typical test cycle, on average 30%-40% of tests fail. Though 93% of these failures are NOT related to defects and are false failures, they need to be triaged by the testing team to understand the underlying reason for these failures. Once the impacted test scripts are fixed, then the automated test run can be deemed a success with either true fails(i.e. defects) or true passes(scripts passed).

Webomates provides solutions to handle such scenarios with ease. Read for more : Test Failure Analysis 

If this has piqued your interest and you want to know more, then please click here and schedule a demo, or reach out to us at info@webomates.com. If you liked this blog, then please like/follow us Webomates or Aseem.