Shift Left Testing

The software development lifecycle must incorporate quicker and earlier testing due to the nearly universal use of agile methodologies (SDLC). ‘Shifting left’.is a term used to describe early integration of development and testing.

You might be asking what I mean when I say “Shift Left” while I’m talking about the program.

When I began my career as a software tester more than two decades ago, there was no such thing as a “Testing Phase” for software development, and the role of the tester didn’t even exist. Developers used to create software and test it.

When production errors began to affect the project’s budget, the idea of software testing was progressively introduced, and “Functional Testing” was implemented with a relatively small team of testers. We were only two Testers compared to a group of 20 Developers at that time.

Since the software development lifecycle is known to proceed sequentially in the sequence of, the IT industry has begun utilizing the waterfall model like Requirements => Design => Coding => Testing.

Traditional testing

The testing would have taken place right before the software was released into production in a conventional waterfall software development process. This implied that the release would be postponed until any bugs or usability problems were resolved.

In this scenario, testing turned into a bottleneck that substantially hampered projects’ ability to finish on schedule.

Introduction to the Shift Left Concept

Over time, individuals came to understand the value of software testing and the consequences of maintaining the “Testing Phase” at the very end or on the extreme right of the software development lifecycle. This epiphany was brought on by the fact that fixing the bugs at the very end and far right were exceedingly expensive, time-consuming, and labor-intensive.

There have been instances where, despite putting in a lot of time and effort, the mission-essential software was unable to be launched to the market because of a critical bug that was discovered only at the very end.

As a result, because the fault was discovered in the final stages of development, either the release was postponed or, in some cases, the software was abandoned because fixing the bug would have required too much work.

There is a great quote about testing: “Defects are less costly when caught early.”

This insight and the important lesson learnt sparked a significant change in the software industry and gave rise to the idea of “Shift Left,” which refers to moving the “Testing Phase” from the right to the left or incorporating testing at every stage and involving testers all the way through.

Shift Left testing also refers to the practice of conducting ongoing tests rather than final ones.

What is Shift Left Testing?

First off, the “Shift left” idea encourages early collaboration between the Testing team and all other stakeholders. As a result, they are able to easily comprehend the specifications and create test cases that will assist the program “Fail Fast” and provide the team the opportunity to address any issues as soon as possible.

The Shift Left approach entails including testers much earlier in the software development life cycle, allowing them to better comprehend requirements, software design, architecture, coding, and functionality. They would also be able to ask challenging questions of customers, business analysts, and developers, get clarifications, and offer feedback whenever it is helpful to the team.

This engagement and comprehension will encourage the testers to learn everything there is to know about the product, consider various scenarios, and create real-world scenarios based on software behavior, all of which will aid the team in finding bugs even before any coding has been done.

Why Shift Left testing?

The delivery and testing requirements are retained on the right side of the plan in the traditional software development paradigm, while the requirements are kept on the left. The issue is that these procedures can’t adapt to shifting standards and specifications, which has detrimental effects on the company’s operations, including:

• Increased costs
• longer time to market
• Unexpected mistakes

Cost is a very powerful motivator for moving your testing to the left. According to estimates, less than 10% of software problems appear during the lifecycle’s development phase, and more than half of all defects could be found during the requirements phase. The cost of fixing these flaws works backwards:

The cost to find and fix a flaw after the product has been released will be roughly 100 times higher than during the requirements phase. According to research from the Ponemon Institute published in 2017, vulnerabilities may cost an average of $80 to fix if they are discovered during the early stages of development. But the same vulnerabilities may cost roughly $7,600 to repair if found after they have moved into production.

The Shifting Left methodology emphasizes the necessity for developers to focus on quality from the beginning of a software build rather than waiting until faults and issues are discovered toward the end of the SDLC. Product teams can complete routine tasks such as:

• Testing
• Feedback
• Reviewing all of changes and the progress made

How Does Shift Left Influence Software Development?

The Shift Left method is centered on including testers in all, but especially in the crucial phases of the program. As a result, the testers may shift their attention from problem discovery to defect prevention and help the program achieve its business objectives.

The shift Left strategy gives testing a high priority, which greatly expands the duties and responsibilities of testers.

As the testing team’s responsibilities grow, the team actively collaborates with the team from the start to plan and develop a robust and effective testing strategy. This is done by offering the team excellent test leadership and guidance and focusing on the long-term vision of the product rather than just taking on the responsibility of the testing work. The Shift Left methodology allows the testers the chance to design the tests first, where the tests are entirely focused on the customer experience and their expectations, allowing the developers to build the software based on these tests and so satisfy the needs of the customers.

The Testers are not the only ones affected by the Shift Left strategy. The Developers will be able to take greater ownership of their code and assume more responsibility for testing by moving to the let and doing the testing activities consistently. The shift Left approach also encourages testers to use test-driven development (TDD) and behavioral driven development (BDD), which helps to stop software defects from being introduced. Agile Shift Left Testing: The Shift Left methodology encourages the creation of Agile Scrum Teams that must include testers along with all other roles. Testers are also included in regular stand-up calls, other interactions, and review meetings, which has given them access to additional program-related knowledge.

Is Shift Left approach in testing always appropriate?

It’s possible that a Shift Left testing strategy won’t always be able to produce the best performance and functioning in a real-world setting. In certain circumstances, a Shift Right testing approach may be used to:

• Increase client satisfaction
• Give direction for implementing test automation
• Better test coverage is ensured

Testing is started by Shift Right from the right, i.e. post-production. To ensure performance and usability, you’ll test a fully developed and operational application in this Shift Right exercise. Reviews and comments from targeted users also contribute to improving the software’s quality.

An essential component of the Shift Right strategy is a readiness to:

• Test a theory’ veracity by implementing fresh ideas
• Work together with customers to figure out what is effective (instead of working from assumptions)

Users’ ongoing feedback could improve how we react to software faults.

Shift Left Techniques

You may shift left with your software testing by using a few important strategies:

Demand planning

With a forward-looking view of demand, test analysts will interact with business and operational stakeholders. With this perspective, you may plan and decide in advance:

• Budget
• Resourcing
• Strategies on testing

Demand planning is a crucial component of the shift left strategy and serves as the foundation for all other test lifecycle tasks.

Static testing

Early on in the project’s lifecycles, requirements and designs are validated as part of static testing. Static testing’s goal is to identify flaws early in a product’s life cycle before they become highly expensive to fix in the project’s later stages.

Utilize the proper checklists to validate the requirements and design. Enter errors in a tool for defect management.

Unified test strategy

This is a comprehensive, high-level testing method that covers end-to-end testing from unit testing to operational readiness testing (ORT), user acceptance testing (UAT), and post-deployment testing. The plan will outline precise roles for each stage of quality control.

By analyzing dependencies on environments, stubs, automation, and test results, you can make sure that the various teams can meet the demands.

Analysis based on risk

For each test scenario, a risk-based analysis is done to estimate the consequences and chance of failure. Functional, non-functional, and regression testing all employ this methodology.

After the test cases are created, rank them in order of importance using the results of the analysis. With the business analyst or designer, go over the consequences of failure. From the development team, ascertain the risk of failure.

Shift Left Testing process is:

• Early fault detection helps keep the project’s cost down.
• Testing continually, repeatedly, and finally reduces defects
• To accelerate time to market and automate everything.
• To concentrate on customer needs and enhance the client experience.

Benefits of  shift left approach

A shift left strategy has a number of advantages that can be attained. Some of the most crucial are listed below:

Automation

Testing can be automated more effectively by shifting to the left. These important advantages of test automation are offered:

• Less human errors
• more thorough test coverage (multiple tests can be conducted at the same time)
• the capacity for testers to concentrate on more engaging and rewarding activities
• less production problems

Accelerated delivery

Faster is earlier. Defects are much easier to rectify when discovered early in the production cycle. The result is:

• The interval between releases may shorten dramatically.
• The software gets better in quality.

Increased contentment

One of the main advantages of the shift-left strategy is faster delivery of software with fewer flaws.

The looks on your business associates’ faces should be enough to persuade you that this is a smart choice if nothing else.

To increase their velocity, quality, and efficiency, Humana chose a contemporary mainframe development environment for editing and debugging code.

Conclusion

The entire “Testing” job underwent a significant alteration as a result of the “Shift Left” concept. Up until that point, the main goal of testing was “Defect Detection,” but now the goal of the “Shift Left” is a trip from “Early Defect Detection to Static Testing.”

As a result, Shift Left represents a Big Leap in the Software Development technique for the software industry in terms of accelerating time to market, enhancing software quality, and decreasing “Time to Market.”

What is User Acceptance Testing (UAT)

A studio will screen a film in advance for test audiences to gauge how well it will perform. Additionally, in some instances, the studios have changed the endings, added additional sequences, and had portions reshot as a result of fan input. People’s experiences and views matter and can have an impact on how a release turns out.

What does UAT stand for?

User acceptability testing, or UAT for short, is a type of test audience screening used in the software business. UAT testing, however, entails much more than just screening a film to test subjects. You will be introduced to the realm of user acceptance testing in this essay as a result. In this section, we’ll address issues like “What is user acceptance testing?” “What are the requirements for user acceptance testing?” “How can I become a user acceptance tester”and “How can we make user acceptance testing more efficient?” We’ll also talk about the difficulties with UATs and how those are different from system testing.

Starting off with a definition will help us understand better.

What Is User Acceptance Testing?

User acceptance testing, also known as end-user, user acceptability, or beta testing, is the practice of having customers or users evaluate software to determine whether or not it is ready for release. The testers can accurately assess the product’s readiness because they are familiar with the business requirements for the software.

When Is User Acceptance Test Performed?

UAT is the final stage of the software testing process, following the completion of functional, system, and regression tests. It is the final test run before the product is released to the public or the client accepts delivery. Consider the UAT to be the bow on top of the testing package; it is not performed until all other testing has been completed.

Acceptability testing includes alpha, beta, and user acceptance testing.

Who does UAT?

Either members of the target market of potential customers or a testing team of end users that includes stakeholders and representatives of every division within the firm make up the testers

What Is User Acceptance Testing and Why Is It Necessary?

Software is produced by technical individuals who follow the relevant specifications. That’s what they do. Even if they follow every instruction to the letter, there are still some aspects, such as business requirements or processes, that only end users would comprehend. Even the requirements for the application are occasionally not understood.

Before the product launches, testing is essential to ensuring that all business needs have been satisfied. Running user acceptance tests is less expensive in the long term than fixing defects and problems after the product has been released.

User acceptance testing enables the application’s provider to ensure that the product works as intended for paying customers and that it is functionally sound. If not, flaws can be fixed and features can be adjusted to match user expectations.

Is there a need for User Acceptance Testing?

After software has undergone Unit, Integration, and System testing, User Acceptance Testing becomes necessary because developers may have built software based on requirements documents according to their own understanding and further required changes during development may not have been effectively communicated to them. This makes it necessary to test whether the final product is accepted by the client or end-user.

Acceptance Testing and the V-Model

The V-Model, which has operations operate in a sequential order to resemble a “V,” includes acceptance testing. Verification and validation models are other names for V-models. User acceptability testing is the last stage, the end of one of the “V” branches.

What Is UAT: Prerequisites and Characteristics of User Acceptance Testing

A studio will screen a film in advance for test audiences to gauge how well it will perform.

• Additionally, in some instances, the studios have changed the endings, added additional sequences, and had portions reshot as a result of fan input.
• People’s experiences and views matter and can have an impact on how a release turns out.
• User acceptability testing, or UAT for short, is a type of test audience screening used in the software business. UAT, however, entails much more than just screening a film to test subjects.
• You will be introduced to the realm of user acceptance testing in this essay as a result.

Describe the main tasks for each phase of the UAT: At this level, the tasks are divided into the following steps:

• UAT Test Initiation: Outline the strategy, who will conduct the test, the necessary data, and how other teams will assist in the test.
• Validate business-related scenarios, find pertinent test data, upload scenarios, and give users access as part of the UAT test design.
• UAT Test Execution: Run the test, look for errors, and perform regression testing and defect re-testing.
• Test closure for UAT. Make the “go/no go” decision and the closing report.

Governance of UAT

The procedure’s excellent gates and clearly stated Entry and Exit criteria are guaranteed by user acceptance test governance.

• Check that the tasks are finished at the entry gates.
• User stories have been finished and approved.
• Testing for regression is done.
• There are specified access and environmental requirements.
• Testing of the system and integration is complete.
• There are validations of the user interface.
• Testers from the business world have been selected.
• A UAT sanity check has been done by the team.
• The functional specifications have undergone validations.
• Ensure that system testing is completely (100%) covered.
• Verify that there are no serious flaws or unresolved show-stoppers.
• The team should make sure that the objectives and end-to-end UAT validation are satisfied.
• Every application is functional and stable.
• Operating systems and browsers work together.
• All applications have preserved the integrity and flow of data.
• All crucial corporate operations are in place and running smoothly.
• The business process has been properly integrated with each transaction.
• Feedback/reviews from business users on the usability of the application’s features.
• The program under test’s actual performance.
• Exit criteria for the UAT must be met by resolving these two problems.
• All flaws discovered during the UAT are fixed and approved.
• Business flows have been acknowledged in accordance with the business requirements established by the system’s end users.

UAT Planning Test

The system and UAT are typically planned in tandem. Making a user acceptability test plan, which includes the test dates, environment, participants, roles and responsibilities, templates, communication protocols, findings and their analysis procedure, and entry-exit criteria, is the main task involved in this activity.

User Acceptance Testing Design

The user acceptability criteria that have been gathered are covered at this stage. A list of test cases, often based on a template with fields for the test number, acceptance criteria, test results, and user comments, frequently makes up this requirement.

Execution of Tests

When possible, the testing takes place in a “war room” or conference setting where everyone gathers for a day (or more) to go through the acceptance test cases. The authorities make an acceptance decision, sometimes referred to as the go/no-go decision, after the testing are finished and the results are finished. It’s “Go” if the users are pleased. It’s “prohibited” if not.

The Qualities of UAT Testers:

The UAT tester should be well-versed in the industry. He needs to be self-reliant and consider himself a system outsider. For the UAT to be successful, the tester needs to be analytical, lateral thinking, and able to mix many types of data.

The ability to generate tests and data that are realistic to the business is a skill that testers, business analysts, and subject matter experts possess.

UAT test cases example:

User Acceptance Testing Tools and Methodologies

These instruments resemble those employed in functional testing.

UAT testing Tools: Users depend on tools for defect and test management like JIRA or QC.

Fitness tool: Making tests and entering results in a table is simple. The tool’s users input prepared data, and tests are generated automatically. The tests are then run, and the user receives the results back.

Watir: It is a toolkit used for user acceptability testing to automate browser-based tests. For inter-process communication between Ruby and Internet Explorer, Ruby is utilized as the programming language.

Methodologies:  A vendor must test a product with a wide range of users before expecting it to be released globally. Crowd testing is particularly effective since it enables consumers from around the globe to participate, confirm the product’s utility, and offer feedback.

User Acceptance Test procces

User Acceptance Test plan in details:

Step 1) Business requirements analysis

Finding and creating test scenarios is one of the UAT’s most crucial tasks.

Step 2) UAT creation plan

The technique to be utilized to confirm and guarantee that an application satisfies its business requirements is laid out in the UAT test plan. It outlines the entry and exit requirements for UAT, the methodology used for test scenarios and test cases, and testing schedules.

Step 3) Determine the Test Scenarios and Test Cases:

Create test cases with precise test procedures and identify the test scenarios in relation to high-level business processes. Most UAT scenarios should be adequately covered by test cases. The test cases are created using the business use cases as input.

Step 4) Preparation of Test Data:

For UAT, it is best to use live data. For the sake of security and privacy, data should be jumbled. The database flow should be familiar to the tester.

Step 5) Run and keep record of the results:

Execute tests and then report any issues. Test again after issues are fixed. Tools for test management can be employed throughout execution.

Step 6) Verify the business goals were achieved:

After the UAT testing, business analysts or UAT testers must submit a sign-off email. The product is ready for production after approval. Test Plan, UAT Scenarios and Test Cases, Test Results, and Defect Log are examples of deliverables for UAT testing.

User Acceptance Testing Template

Conclusion:

• User Acceptance Testing (UAT) is the full version of UAT in software engineering.
• One of the many types of testing that have evolved during the past 25 years is UAT.
• Instead of guessing, the client can be certain of “What to expect” from the product thanks to UAT.
• There won’t be any unpleasant surprises when the product is made available to the public thanks to UAT.

Artificial Intelligence (AI) vs. Machine Learning (ML)

Artificial Intelligence (AI)  vs Machine Learning (ML) is a somewhat controversial topic which has been debated by experts and numerous others in the industry. AI and ML is frequently used to give clarity regarding features, capabilities and distinguishes one from the other.

But what is Machine Learning and Artificial Intelligence?

Artificial Intelligence

What is Artificial Intelligence?

Artificial Intelligence (AI) is a field of software engineering which creates a PC frame that can emulate human insight.It is involved two words “Artificial” and “Intelligence”, and that signifies “a human-made speculation power.”

Artificial Intelligence (AI)  framework does not requirea pre-programmed system, it uses different algorithms which work by their own intelligence.

Many of the technologies we use today, such as smart devices and voice assistants like Siri on Apple devices, are powered by artificial intelligence. Businesses are using methods like natural language processing and computer vision, which allow machines to understand images and understand human language, to automate jobs, speed up decision-making, and enable consumer exchange with chatbots.

Three categories of AI can be distinguished grounded  on their capabilities:

• Weak AI
• General AI
• Strong AI

In the meantime, we are using both general and weak AI in our work. Strong AI, which is the direction of AI, is predicted to be more intelligent than humans.

Where is Artificial Intelligence used?

Manufacturing Industry

The success of a company in the manufacturing sector depends on its efficiency. By utilizing data analytics and machine learning in applications like the following, artificial intelligence can assist industrial leaders in automating their business processes:

• Using analytics, machine learning, and the internet of things (IoT) to detect equipment flaws before they cause problems
• Utilizing an artificial intelligence (AI) application on a machine in a factory that watches a production machine and forecasts when maintenance needs to be done to prevent failure mid-shift.
• employing machine learning to analyze HVAC energy consumption patterns and make adjustments for the best possible energy savings and degree of comfort

Banking

The banking sector places a premium on data security and privacy. Financial services leaders can use AI and machine learning in numerous ways to protect consumer data while boosting productivity:

• Machine learning is being used to identify and stop fraud and cybersecurity assaults.
• Using biometrics and computer vision to process documents and swiftly verify user IDs
• Automating routine customer service tasks using smart technology like bots and voice assistants

Health Care

To deliver precise, effective health services, the healthcare industry consumes enormous volumes of data and increasingly relies on informatics and analytics. AI solutions can assist healthcare professionals avoid burnout, enhance patient outcomes, and save time.

• Analysis of user’s electronic health records to provide automated insights and clinical decision support
• Using a system that anticipates the results of hospital visits to avoid readmissions and cut down on the amount of time patients are held in hospitals
• Utilizing natural language understanding to capture and record patient-provider interactions during examinations or telemedicine consultations

Machine Learning (ML)

What is Machine Learning (ML)

AI is used in machine learning. It is the practice of assisting a computer in learning without direct instruction by applying mathematical models of data. As a result, a computer system can keep picking up new skills and getting better on its own. By examining and exploring different paths regarding AI, developers test the restrictions of the amount they can work on the discernment, insight, and activity of a PC framework. Where as Deep Learning is an advanced method of the ML and  goes beyond.

Is Machine Learning the same as AI?

No, machine learning is not the same as an ai but it is part of AI. ML is and will always be considered a subset of AI.

Some examples of Machine Learning (ML)

Speech recognition

It is the machine learning what makes speech transform into text. There are certain software applications that can convert voices or recordings intro a text.
Real-life instances of Speech recognition:

• Voice Search
• Voice dialing
• Phone assistant

Image Recognition

A well-known and common application of machine learning in the real world is image recognition. Based on the intensity of the pixels in black-and-white or color photos, it can recognize an object as a digital image.

Real-life instances of Image Recognition:

• Decide whether an x-ray is malignant or not.
• Tagging on social media recommendation.
• Handwriting recognition

Predictive analytics

Available data can be categorized by machine learning into groups, which are then further defined by rules established by analysts. The analysts can determine the likelihood of a fault once classification is complete.

Real-life instances of Predictive analytics:

• Determining whether a transaction is genuine or fraudulent
• Improve prediction methods to determine the likelihood of a fault

One of the most promising applications of machine learning is predictive analytics. It can be used for anything, including pricing for real estate and product development.

Are Artificial Intelligence and Machine Learning the same?

Despite their close connections, machine learning and artificial intelligence are not the same. A part of artificial intelligence is machine learning. AI is used by “intelligent” computers to mimic human thought and carry out independent activities. The process by which a computer system becoming intelligent is called machine learning.

Difference between Artificial Intelligence (AI) vs Machine learning (ML):

The majority of regular people frequently confuse the terms artificial intelligence & machine learning and use them interchangeably. Even while machine learning is a component of artificial intelligence, these two phrases essentially refer to two separate concepts. That’s is why there is a concept of Ai vs ML.

Conclusion

Even if the majority of people confuse them AI and ML technologies are vastly different as we mentioned above. Which of them is more important ? That’s up to each of us to decide. The main AI and ML difference will always remain that ML always needs a new set of data to advance while AI will try and solve problems just like you and me would.