impact qa

industries for qa and software testing services | impactqa

Being a top software testing company in New York, we offer solutions for various retail and E-commerce businesses that help them improve their internal processes. Get free consultation for Retail and E-commerce testing services

ImpactQA helps in implementing guidelines & best practices in software testing life cycle of medical devices. We specializes in improving and optimizing the QA for large Medical Device manufacturers. From managing test governance to reducing release defects, our team ensured the quality management system of medical devices. We not only help you get FDA, QMS and Audit QA Governance approval but optimizing V & V efforts in medical software and devices.

about impactqa | top quality assurance and software testing company

ImpactQA is the global leader in next-generation software testing and QA Consulting. We help SMEs and Fortune 500 companies to deliver digital transformation and technology services, enabling global 250+ clients to stay one step ahead of disruption. Our experts redefine emerging technologies and business practices to excel in areas of digitalization, automation, engineering and containerization. We have unmatched testing capabilities across many industries such as Healthcare, E-learning, BFSI, Ecommerce, Media, Logistics, Real Estate, Medical Device Testing and more.

Whether you are a Fortune 500 company looking for testing solutions or a start-up squashing the status quo, we provide our end-to-end software testing solutions. ImpactQA provides clients a vast pool of QA experts at a cost nearly 70% more cost effective than western markets. Our methodology, tools, frameworks and 200+certified QA experts are here to work for a fraction of the cost. We have in-depth knowledge of functional and non-functional testing merged with web, mobile application and cloud technology.

Our in-house testing team has an international reputation for delivering cost-effective, insightful and business solutions to a vast array of SMEs and Fortune 500 Companies. With almost a decade of experience, we have successfully expanded our business across different testing domains, namely automation testing, performance testing, mobile app testing, cloud testing, IoT testing, security testing, DevOps testing, AI testing and more. Our expertise fuels data-driven acceleration from automation to devOps to drive speed, innovation and transformation across the digital product life cycle.

contact us | get free qa consultation | impactqa

I have been working with ImpactQA for 2 years now as my key QA contractor. The three ImpactQA contractors that work with us have become integral members of our team. They have gone far beyond any QA contractors I have worked with before - they have helped refine our process, bring in new tools, and are always thinking critically and proactively how they can help my company succeed. All three are highly experienced (8+ years or more) and have excellent communication skills. They are also backed by an excellent organization, with hands-on executives who regularly check in to make sure we are happy; despite our small size (relative to some of their other clients), their CEO always visits us in person whenever he is in town. ImpactQA is truly a great vendor and partner.

We chose ImpactQA for handling our performance and security testing services. Their team made sure there were no performance bottlenecks and security issues in our applications as we were moving them from DCs in Singapore over to the cloud. They were professional, robust and exceptionally good in delivering their testing services.

We are very pleased with the work and professionalism from the beginning to the end. We have seen tremendous value with the outcome generated by the team with very limited supervision and bandwidth from our side. We definitely will be looking to engage with the team again in the future as we roll out new products and make huge enhancements to current products.

what is impact analysis in software testing?

Have you ever felt a desire to take some mechanism apart to find out how it works? Well, who hasnt. That desire is the leading force in reverse engineering. This skill is useful for analyzing product security, finding out the purpose of a suspicious .exe file without running it, recovering lost documentation, developing a new solution based on legacy software, etc.

This article is written for engineers with basic Windows device driver development experience as well as knowledge of C/C++. In addition, it could also be useful for people without a deep understanding of Windows driver development.

The following article will help you to understand principles of Windows processes starting. In addition, it will show you how to set some filters for process start, including allowing and forbidding ones.

In this article, we will tell you our own way of Impact Analysis introduction and work with it. Why did we decide to introduce Impact Analysis in software testing in our teams? What practical benefit from this innovation have we got and are getting today? You will get answers to these and many other questions concerning this topic while reading this material.

We devise and implement different features, additional means that can be useful for a user. We work with the constantly changing acceptance criteria: we develop something, then change it, add something, remake something, etc.

In such continuous process of development, it is possible to face an unpleasant situation when it is difficult to track consequences of changes and modifications being introduced: to estimate what parts of the program may be affected and how significantly they may be affected.

As a result, testing is performed accordingly to the best practices, but some part of the product, some module or feature are looked over or checked not deeply enough, just because of the Impact Analysis in testing information absence.

Information presented in this article will be useful for those specialists who need to analyze, what product items made changes can influence on; i.e. for everyone who is related to IT product development.

It is safe to say that after reading this article you can figure out how to easily make your product better. If talking about Apriorit, we already rely on the Impact Analysis within all our software testing and quality assurance processes - you can learn more details on them here.

Briefly, the Impact Analysis is used in software testing to define all the risks associated with any kind of changes in a product being tested. There exist several software Impact Analysis definitions that accent different aspects. It will be useful to consider each of these definitions because this will help you to make a decision and define to which of these definitions your personal Impact Analysis will be closer.

The first definition says that Impact Analysis means detection of potential consequences of changes or things that must be remade together with the introduced changes. Here, Impact Analysis is considered in terms of changes in the product.

According to the second definition, Impact Analysis is an estimation of the number of risks associated with the product changes, including the estimation of influence on resources, work and timetable. This Impact Analysis definition takes into consideration changes consequences in terms of the whole development process.

If we consult with ISTQB glossary, software Impact Analysis is an estimation of changes on different levels of development documentation, testing documentation and components for introduction of these changes and registration them in corresponding requests. Here, our attention is drawn to the fact that changes should be taken into consideration on all documentation levels: from the code to requests.

In the development process, any interaction method between developers and testers would be useful. At times, testers dont receive full information about introduced changes. This fact, in turn, influences directly on the product testing reliability. It is Impact Analysis that is required to solve this problem.

Without Impact Analysis, testing specialist can use those test cases that in truth dont cover last changes in the project. At the same time, he/she might not pay his/her attention to testing of those project parts that really require it.

It turns out that Impact Analysis in testing helps to decide on which areas to focus time and resources. Therefore, it is a very powerful tool that allows QA to considerably increase testing efficiency.

Before the Impact Analysis introduction, the communication scheme between developers and testers inside a project was slightly defective. Usually, after building of a new product version, project development team sent a testing request. This request contained the list of fixed bugs and link to the new version location.

Most often, the project is developed by several developers, each of which works on his/her task. Therefore, a new version is a merged result of several programmers work. But testing request is sent by that programmer who builds the version. So, as a rule, this programmer knows only his/her fixes and changes.

Thus, before the introduction and formalization of work with Impact Analysis, testing request contained no information about those parts of the project that were influenced by changes, and about their influence on other features. But even if such information was given, we could not consider it full and reliable. Moreover, such situations were rather an exception than a rule. And of course, there was not any guarantee that a programmer had taken into consideration all features, modules and functionalities, which had been or could have been affected in the process of his/her work with the project, especially in work process where other programmers work with the same version. At best, request for testing contained a brief description of changes made by the developer who initiated a new version build, and recommendation for more careful testing of the weak spots only according to his/her changes.

Before the Impact Analysis introduction, we made decisions on testing basing on some kind of empiric criteria. For example, a tester knows that if feature A has been changed than there might be problems in feature D. At the same time, feature A never was related with feature B. Nevertheless, a tester might not know that the A and B features are for all that related with each other and after certain changes unexpected bugs may appear. In addition, if there is a lack of communication in the project, a tester may not know at all that feature A has been changed because behavior was similar to the bug of feature C.

Also, there were difficulties in defining testing priority. I.e. the question was: what part of the functional required full testing, what part smoke testing, and for what part wild or acceptance testing was enough.

So, we have already told what situation we had before introduction and formalization of Impact Analysis usage. The problems described above were exactly the first reason to think about introduction of this innovation into the process of our products development. Also, it is rather popular technique in the modern SQA world.

Having discussed this question in details, we understood that it is a very useful practice that brings positive results almost in no time! Hence, here is the conclusion: it would be better for us to make this important step on the way to increasing our products quality and to adjusting product development process. All the more so, making this step is easy and convenient because some of us have already did the things that are included in the Impact Analysis process and worked with its results, but they did it intuitively and not completely, without formalization and centralization. Its time to change it!

Initiative group of our specialists gathered a meeting where it was decided to begin development of the Impact Analysis strategy introduction into our work process. Some people were appointed to be responsible for this question in their projects. Also, the term, within which the development of the principles of usage and work with the Impact Analysis in each project must be finished, was determined.

Specialists responsible for this question held meetings in their project teams, as well as drew up the concrete goals, specifics and the ways of achieving the desired results via Impact Analysis introduction. The opinion of each team member was taken into consideration. Some important and interesting moments of the Impact Analysis introduction process will be discussed below in more details.

The QA specialists together with all other project participants developed such tables for each project. As a rule, such tables contain the list of features/modules/functionalities of the product. Before sending the version for testing, developers mark the corresponding features/modules/functionalities that were influenced/could be influenced by the introduced changes in the same table.

As you can see, the template is a matrix. All features/modules/functionalities, which can be singled out in this product (for example: installation, uninstallation, update, hot keys, Menu, Toolbar, Hints, Options, etc), are enumerated in this matrix horizontally and vertically. Vertically, we define those features that have been changed. Horizontally, we define the features, which performed changes can influence. Change of each feature influences itself and it is defined in the table diagonally beforehand.

So, lets suppose that a programmer knows what he/she has changed in the feature1. He/she looks at the given template and performs the analysis. The first we have already clarified is the fact that changes in the feature1 mostly influence the state of the feature1 itself. Later, the developer understands that changes in feature1 influence the state of feature3 and can influence the state of feature2. A specialist writes down all his/her thoughts in this matrix in the line for feature1. The same analysis is performed for each feature being changed. Analysis results are marked in a line corresponding for this feature.

Correspondingly, a QA specialist plans his/her work more thoroughly after he/she has received such a table. Using data from such Impact Analysis table gives an opportunity to prioritize testing tasks. This is especially important if there are strict time limits for the product testing.

Due to this example, a QA specialist can immediately notice that features 1, 4, and 6 need to be checked in details at first, features 3 and 5 in the second turn, and feature 2 should be checked the last and maybe not in such details. Such analysis and planning provide the decrease of our risks considerably when the product is to be released within strict time limits. This ensures us that the most critical and important things will be tested. Having data from this table, we will never start testing from the low-priority features.

Now, lets examine such example: we have a large-scale project, where there are a great number of features/modules/functionalities. And additionally, each module includes a certain number of sub-modules/features/functionalities.

For such projects, it is not reasonable to work and track the results of Impact Analysis with the matrix discussed above. For example, we have 40 main features; each main feature additionally has 15 features. As a result, we have such 600 x 600 table:

Therefore, we developed a special form of the Impact Analysis table for such projects. In rows, it contains all main features/modules/functionalities that can be singled out in the project. In columns, it contains all sub-modules or all features that concern each of the main features enumerated in rows.

For large projects with a number of features and functionalities, we use the table where a developer doesnt mark exactly what feature was changed. He/she immediately marks which other features or functionality it influenced or could influence.

And typically, if the changes made by a developer influence the Sub-Feature1, it doesnt mean that they also influenced all other sub-features. We can see this from our table for Example 2. Here, the changes made by a developer influenced only the Sub-Feature1, Sub-Feature3 and Sub-Feature4 and didnt influence other Sub-Features.

Finally, lets answer the main question: What is our formalized system of work with Impact Analysis? It is an accurate succession of actions that each specialist in the team must perform. The main advantage of such method is that each of us has a clear understanding of his/her duties, and our expectations from each other are fully synchronized.

We have two efficient schemes of work with Impact Analysis we would like to share. What is the difference between them? The matter is that the system of version auto-build is absent in the project, where the first scheme is used, and in the second project, this system is present. Therefore, the filled Impact Analysis table attached directly to the testing request in the first case, and in the second case, it is stored on the server with the prepared versions.

It is good for two reasons: firstly, a developer is still in the context of the problem and can give the most reliable information; secondly, he/she has an accurate check-list and information will not be lost or forgotten.

After the build, the system copies the table in the corresponding project folder on the server with prepared versions. Thus, the system monitors that information in the table corresponds to project versions and allows the developers avoid unnecessary manual work.

We use both these algorithms in different projects. They are the verified and smoothly running schemes. Its usage gives us the confidence in information that we receive and increases the quality and speed of our work.

As we promised, lets return to the process of the Impact Analysis introduction in our QA life and answer the questions: how we performed this and what interesting moments we faced during this process.

The fact is that Impact Analysis in software testing is an effort to receive information from the developers that is based on project architecture knowledge to define testing scope, level, and sequence.

So, after first opinions were received, we gathered another meeting as for the Impact Analysis and discussed all pros and cons. Surely, everyone understood that Impact Analysis is very useful and necessary both for testers and for developers. However, the list of features in projects was discussed for about a week. Such questions were discussed: how small should the features be? What features to merge and vice versa, what features to separate into smaller ones? Why not to make the list in the form of tree? What features are connected and what features are not?

Not to sound proofless, we want to describe the real-life example and tell what practical benefit the introduction of the Impact Analysis procedure makes for us. This example is very simple. But it is noteworthy because it was noticed by one of our developers.

Later, we will use a certain quality metric that is called Quality Level (QL). It is the metric that we constantly use for our projects practically every day. For those, who does not face it very often or maybe has never heard about it at all, we will make a small digression and explain what it is.

A tester received the mentioned request and performed testing of the feature specified in the request. The feature worked perfectly. A tester didnt find any bugs. He sent the testing response where he wrote that bugs were not detected and QL = 100%.

At first sight, everything was just perfect. But some time later (it is good that not after release), it was discovered that changes in this feature led to break in other feature, because they were closely related with each other.

Whose fault is it in such situation? Is it the fault of the developer who did not check the influence of introduced changes on the dependent feature? Or is it the fault of the tester who did not come to an idea to check the feature, which the changes could influence? It is a rhetorical question; there is no use to look somebody to blame. The important thing is that the quality of the product, on which both testers and developers were working, suffered.

According to the words of the same developer, who described this example, even if the additional information about what change affect had been present in this request, it would not have rescued the situation. Firstly, it is not obvious that the developer analyzed all possible influences of the change without a check-list. Secondly, the information about the influence of performed changes on other product parts was considered to be rather additional than the main one; thats why, it was considered to be less important both by the tester and by the developer. When the tester, basing on his personal experience, nevertheless analyzed the influence and tested parts affected by the changes, the developer could just overlook this information in the testing response. Even more, it was written somewhere at the end of test response and did not catch the eye.

Firstly, a developer, when sending a request, analyses the influence of the changes. Secondly, performing this analysis, a developer never forgets about the features, which introduced changes influenced, because he/she will see all these features in the Impact Analysis table. Thirdly, what is the most important in this example, a programmer would never overlook information about testing of those parts of the product, which the changes influenced. Why? Because now, the QL value includes not only results of testing of the main feature, in which changes were made. Our new rule is to take into account everything that was tested in QL, i.e. the feature state and the state of everything that was marked by developer in the Impact Analysis table. And if the QL value is decreased because of some bugs found in the part, influenced by changes, developer will surely see it.

Thus, the estimation of the project situation became more accurate and corresponds to the reality. Now, our pseudo-100% wont cheat the developer. Even more, developers wont need to spend a lot of time and search for important information in our testing responses.

By the way, I want to say a few words about reports. The Impact Analysis introduction was a reason to perform one more quality change in our work. After we created Impact Analysis table templates in each project, formalized the procedure of its usage in testing requests and testing responses, we thought about such question: why not to standardize the requests and responses? We thought about it and did it.

After discussion with developers, we worked out the standards and rules for testing requests and responses (these are common for all team members). Now, our requests and responses have an accurate structure and identical design. This helps all members of the development team to spend less time on reading the received information, improve its perception and raise the quality of our communications.

With introduction of Impact Analysis and the formalization of work with it, we resolved those problems that we faced earlier, made our work more efficient and increased our products quality. Now, we feel ourselves much more satisfied by the general work process and its results. You can learn more about software Quality Assurance tasks we resolve in Apriorit in this section.

If you face the situations and problems that are discussed in this article, use our experience for your good. Take the knowledge we shared as a basis and work out a system, which would fit each member of your team. And then, the positive results will not keep waiting for them!

top software testing services company | impactqa

Global leader in next-generation software testing and consulting with AI based automation, quality engineering, continuous testing, and cloud migration SAP testing. Decades worth of testing and technology experience that help enterprises reinvent themselves. The team at ImpactQA applies both domain-specific and client-oriented approach and tools to deliver reliable and quality solutions. We enable clients to navigate their digital transformation globally.

Our AI-enabled intelligent automation testing services can empower your business through automated test releases, accelerated time-to-market, reduced cost, maximum test coverage, multiple platforms testing and outstanding end product quality. Our software test automation approach has saved many projects from failure by addressing QA bottlenecks in the light-fastening speed and time. So, if youre looking for premium test automation company, leverage our 250+ QA testing experts.

From load testing, volume testing, stress testing, configuration testing, stability testing to scalability testing, ImpactQA delivers the high-end performance testing services for managing software performance, system availability, improved UX and software TCO. Our experts ensure resolving performance issues, efficient performance monitoring, controlling application productivity, system architecture planning and mitigating DoS risks.

Our testing experts enable you to perform security testing of mission-critical business processes scanning multiple web applications and systems. From security assessment, vulnerability scanning, pre-certification security audit to static code analysis, our penetration and vulnerability testing services help you to secure online transactions, prevent unauthorized access, minimize the risk of data loss and enhance resistance to DoS attacks.

Our Android and iOS testing services help you to detect application bottlenecks, detecting weak spots and identifying security issues for providing unbiased mobile application testing services on all platforms, browsers and devices. We can help you manage, analyze and execute tests in real-time engineered with DevOps or CI/CD tools.

We test your web, application & software on any device, OS configuration & network security to prioritize eliminating critical bugs before they reach end-users. Our web app usability testing services help in tracking all the QA bottlenecks in the software and mobile applications in order to resolve the UI/UX issues.

ImpactQAs automated functional testing services helps you to ensure the validation of web applications for startups and enterprises. Our manual and functional testing services are based on testing the applications against defined specifications and defining the transparent QA process, seamless integration and robust functionality.

The Next-gen QA and software testing services helps you define QA process, structure and execute problems before they affect your business, ROI and quality. Driven by AI (Artificial Intelligence), IoT (Internet of Things), RPA (Robotic Process Automation), and blockchain technology, we can help you to provide low-cost, on-demand application quality without compromising on the speed and agility.

From planning a move to S/4HANA or the cloud, ImpactQAs modern, AI-driven test automation helps you to boost SAP innovation. Our SAP testing services help you to deliver faster, scalable and reliable software releases while innovating faster business outcomes. We have a track record of helping enterprises through successful SAP S/4HANA migrations.

ImpactQAs AI enabled cloud labs help you to validate end-to-end business processes, stimulate real-time scenarios and identify unexpected bugs across all enterprise applications. ImpactQAs team of experts help global industry leaders discover innovative ways to lead the digital age.

Our QA consulting and outsourcing testing services help you plan, build and implement software testing solutions for multiple business domains from healthcare, fintech, eCommerce, education, media and more. We provide dedicated QA teams for mid-term and long-term projects and offer combinations of onshore and offshore testing at a reduced cost

ImpactQA is a leading independent software testing company. We have helped 250+ clients to navigate their digital assurance services. With nearly a decade of experience in providing software testing services to SMEs and Fortune 500 companies, we expertly embark our clients through their digital journey. We do it by enabling the enterprise with cloud innovation, IoT enabled test labs, test automation, performance engineering and continuous testing with agile digital at scale.

github - best-practice-and-impact/qa-of-code-guidance: guidance for quality assurance of code for civil service researchers and analysts

If you'd like to contribute, please also create or comment on an issue to describe the changes that you'd like to make. This will allow discussion around whether content is suitable for this book, before you put the hard work into implementing it.

To start contributing, you'll need python installed. If you sit outside of BPI, the you'll need to create a Fork of this repository to make changes. Once forked, you should clone the fork repository to get a copy of the book. Then install it's Python dependencies like so:

Any content that is in early development, should be kept under the early_development/ directory. While content that is ready for publication belongs under book/. All pages in book/ must be referenced in _toc.yml or a warning will be raised and the changes will not be published.

You should create a new branch to collect related changes that you make. Once you're happy with any changes you've made to the book, you should raise a Pull Request (PR) to the master branch of the main repository. The source branch of this PR should be the fork and/or branch that you have commited changes to.

devops testing tutorial: how devops will impact qa testing?

DevOps is a combination of Development & Operations it is a Software Development methodology that looks to integrate all the Software Development functions from development to operations within the same cycle.

Although there are subtle differences between Agile and DevOps Testing, those working with Agile will find DevOps a little more familiar to work with (and eventually adopt). While Agile principles are applied successfully in the development & QA iterations, it is a different story altogether (and often a bone of contention) on the operations side. DevOps proposes to rectify this gap.

Now, instead of Continuous Integration, DevOps involves Continuous Development, where the code was written and committed to Version Control, will be built, deployed, tested and installed on the Production environment that is ready to be consumed by the end-user.

This process helps everyone in the entire chain since environments and processes are standardized. Every action in the chain is automated. It also gives freedom to all the stakeholders to concentrate their efforts on designing and coding a high-quality deliverable rather than worrying about the various building, operations, and QA processes.

Traditionally, QA would get a build which is deployed in their designated environment and QA would then commence their Functional & Regression testing. The build would ideally sit with the QA for a couple of days before the QA sign-off on the build. All these steps change in DevOps.

As already mentioned, DevOps requires a high level of coordination between various functions of the deliverable chain. This also means that the boundaries between various roles of contributors in the chain become porous.

DevOps encourages everyone to contribute to the chain. So, amongst other things, a dev can configure deployments. Deployment engineers can add test cases to the QA repository. QA Engineers can configure their automation test cases into the DevOps chain.

To achieve such speed and agility, it is important to automate all the testing processes and configure them to run automatically when the deployment is completed in the QA environment. Specialized Automation Testing tools and continuous integration tools are used to achieve this integration.

QA should also be able to detect problems early and report them proactively. To achieve this, they need to set up monitoring on the Production environment to be able to expose bugs before they cause a failure.

For Example, if the average response time for login is gradually increasing over the various builds, QA should proactively report this issue for optimizing the login code, else future builds might cause end-user frustration due to high response times.

QA can also use a small subset of existing high priority test cases to be executed periodically on production, to actively monitor the environment. Bugs like, This bug appears sometimes or Cannot Reproduce can be caught through this strategy which, in the end, makes the application more stable and also gets more satisfied end-users.

You need to master the various automation and continuous integration tools so that your automation efforts add value to the chain and are lean enough to quickly adapt to changes. You may be working on projects that may involve alpha, beta and UAT environments before being deployed in the production environment.

The concept essentially remains the same. Automation and more automation is the core of a successful DevOps cycle. But, as a QA you should also be able to draw a line as to how much automation is too much automation.

About the Author: Aniket Deshpande is workingas a QA Manager at AFour Technologies, Pune and has been working in the software testing field for the past 9+ years in various domains and platforms. He is passionate about DevOps and is working as a consultant to guide organizations in adopting DevOps testing strategies.

Hi Siddharth, I feel there can be no product which cannot support automation, as all the web, desktop and mobile are covered. But if you still have something that cannot be automated, you can still implement devops as QA is a part of devops and the continuous integration part can still be implemented and your test team can receive builds for testing asap. As far as continuous delivery is concerned, it still needs some work as there might be other tasks like ORT etc that need to be done before sending the product to the customers for usage

Hi Siddharth Spehia, Automation is the corner-stone of a successful DevOp cycle. There are various ways in which automation can be achieved like Non-UI automation or API automation, etc. Obviously, you cannot have all your tests automated, and DevOps does not mandate this. But all your high-priority, end-to-end tests need to be automated and added to the cycle. Talk to your developers to help you achieve this. Ideally, you should start your automation scripting along with early dev builds so that you can provide feedback to the developers regarding blockers for automation.

Hi Smriti, Apart from the regular automation testing tools you can take a look at some of the Continuous Integration tools, like Jenkins, TeamCity, Team Foundation Server, etc. You should be able to configure your automation runs through these tools.

Smriti, since weve been working on Deployment Manager (a release automation tool) at Red Gate weve found that DevOps and QA (or testers) have more and more overlap. For instance weve discovered that a lot of our customers tend to set up environments specificially for QA and/or DevOps that they use to pass deployment packages to each other.

Hi.. Nice article.. I would like to see the feedback loop illustrated in the process diagram. Does the cycle happen in one Sprint? I am trying to understand how different is this from Agile combined with process improvements made by teams over a period of time. Have we just standardized the same and termed it as DevOps?

Hi Girish, Thanks for the feedback. You can call DevOps as more of a philosophy than a methodology and it imbibes most of its processes from Agile, which in-turn is more of a philosophy than a methodology. I guess you are right in a way when you say that it is Agile combined with process improvements made by teams over a period of time. But things like environment standardisation, full automation of the entire build-test-deploy cycle up to Production are somethings that DevOps tries to address. DevOps makes Agile more agile.

Good article Aniket It would be so kind of you if you can tell us how DevOps can address the two challenges we face in agile or any paced up environment: 1.Frequently changing requirements till system stabilisation. 2.Automating initial round of testing in less time.

Hi Hari, You need to start automating from the initial cycles. This will help you in achieving your automation milestones sooner. Also, if there are frequent changes in requirements, dev would require multiple builds to stabilise their code. QA can also use these interim builds to stabilise their automation code before it is finally Production ready. Again, close coordination and communication between the dev & QA team is the key

Hi Swapnil, Thanks for the feedback. Developers will either fix bugs or implement new features (User Stories). That is what is depicted in the diagram. Essentially, anyone in the chain can raise a bug (not just QAs).

Hi, One doubt is how we can use this for 1st sprint where we will not be having for application during 1st deployment. So we cannot automate our test cases for 1st sprint till deployment is done. So I wanted to know how we can integrate automated test cases in Devops for 1st sprint.

Write your BDD feature file when you know the scenario after reading requirement At first run it will fail then refactor by implementing the step def and page object it will still fail if the developement is not done get the object details and update the object repo. when development is done; run your test should pass.

Hi, Thanks. Wonderful article to understand the DevOps which is future. Two Questions if you can help please. 1. Early automation sounds good but in actual, while requirements are changing frequently, there will be too much rework in updating the automation scripts. Generally automation is advisable when system is matured. How beneficial it would be ? 2. How can we automate test cases for first sprint when application is not ready ? To achieve this, I think FSDs should be detailed enough but again that is difficult when requirements are changing frequently.

In which Sprint one can do the E2E business process testing?. For an example, like Order to cash business process in the typical enterprise setup where multiple applications are involved. Especially when the user stories for a release requires the code change only few application in the stack of application to complete E2E business process. In some places, they run different sprint for E2E (Runs once in every four Sprints or where there is a logical end) and others run after the last sprint like waterfall model. I feel this defeat the purpose of Agile. Any one has better solution?

Hi Aniket, We are a team of 5 developers working for a telecom client project. We are to give integrated devops as a part of the deliverable along with the work assigned. I need to know whether in a devops culture are the developers supposed to do QA as well, Write Test cases themselves & also write the selenium automation scripts themselves for CI CD? What does devops principles say? Should there be a dedicated Dev & QA team along with Devops engineers who could wear bot the hats of Dev & QA. Are the Devops engineers part of Dev Team or QA team or are shared resources or are they a dedicated team separate from Dev & QA teams? Now since I have just 5 developers should I train them in QA & in Devops automation & make them do all the work of Dev QA & Dev ops? Its going to be tightfisted situation if they are to deliver in weekly sprints as well as write QA test cases & do automation in the same week for the sprint. Should I propose the Client to allow me to add a team of QA engineers who know selenium & also know automation & let the 5 developers focus on Dev stories. Ofcuorse once the QA comes in then the Dev will start learning Selinium & contributing to the QA & the automation part.. Will this proposal be violating Devops principles that the developer needs to do QA & automation both in a typical Devops setup? Please suggest how to deal with this situation & what exactly are the devops guidelines for the duties of a developer, a QA & automation team. Or is it just one Devops team which does all 3 things?? Or is it a dedicated Devops team of engineers who take care od this integration of Dev QA & Deployment?? should the devops engineers be a part of Dev or QA team or are they a part of different devops team I am really confused when it comes to resource allocation & resource billing for the client in a typice devops setup. Please advise.

Very interesting document, anyway there is still a misusage of the word QA, and test and abuse of the word test automation. QA is validation, test is verification, proactivity vs. reactivity. If you want to achieve a DevOps establishment you should rethink the way you write the software, modularity independency and resilience are the key factor, then you think towards BDD, you will get your software automatically tested. If you have for example 10 agile teams each working in a separate product using LeSS framework, then I would like to understand how you ensure the the final product match with the consumer expectation and how you want to improve such a product. In this case to me QA means sit close to product owners and understand their expectation and proactively work to collect and maintain the focus on the roadmap, looking if the mvp match with the consumer expectation. Test automation and all the means to ensure the bugs are caught during the implementation it is part of the software development process, QA will give the methodology and the structure, DevOps team are implementing it and collaborating with QA to improve it.

QA changes for DevOps Testing: not sure how is this different to any other Dev Methodology. Regardless if this is Waterfall or Agile-like Methodlogies you want to automate deployments/testing/clean-ups to the best extent you can. Needless to say that environments have to be standartized as well. So what is so specific here for DevOps? Just trying to the uathors point of view.

Great work, thank you for providing this valuable information in the form of tutorials, it is awesome. I have been clicking ads here on purpose so that you can get paid for the Excellent job you have done. Thank you.

hello, Since you are a QA Manager, do you have separate QA/Testers in the DevOps setup? Or this is shared on a Developer only? Meaning These Developers are multi-skilled at programming and testing. They can code review each others tasks and test each others tasks as well. Have you experienced a setup where in DevOps, there are still separate roles on BA, Dev & QA? Thanks.

Context On a project where a new system is being built and you have a large number of user stories / business requirements, along with functional specification and business rules, traditionally we (Software Testing function) would end up with a large number of Test cases / Test Scripts (say for example 650 tests scripts) This would require: -We write the Test cases, -Get them reviewed and signed-off -we develop all Test cases into Test scripts With a team of 4 we can achieve the above in circa 20 days

Conerns: -Time taken to decide which tools to use for automation -Time it takes for Test Automation Specialists to understand/learn the system being built -Time it takes for Test Automation Spcialist write the 650 Tests and get them reviewed -Time taken to build and test the automation framework -Time taken to automate the 650 tests and to test them to ensure automated code works -Time taken to run the tests (this is the value added, but it seems an awful long road to get here)

The Philosophy of Continuous integration (CI) is to make small code changes and check-in code frequently to a central repository and then ensuring that you are making progress in terms of features (or bug fixes) while not breaking any existing functionality. To be able to check if no existing functionality is broken is to check this frequently via automated tests. Thus, CI can meaningfully exist only when there is adequate automated testing.

The Philosophy of Continuous integration (CI) is to make small code changes and check-in code frequently to a central repository and then ensuring that you are making progress in terms of features (or bug fixes) while not breaking any existing functionality. To be able to check if no existing functionality is broken is to check this frequently via automated tests. Thus, CI can meaningfully exist only when there is adequate automated testing.

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how ai and machine learning impact software qa

Every company wants the future of technology implemented today. Thats why the AI industry rapidly grows year after year and continues to be one of the biggest automation testing technologies trends in software testing.

Adding machine learning and AI to your QA testing strategy can be both exciting and scary. Its always fun to engage with new technology and discover all the key benefits of AI in QA test automation. But what impact does AI and ML bring to software QA? And should that change be embraced or rejected?

Artificial intelligence is the ability for machines or programs to carry out tasks in a smart way. More specifically, AI is demonstrated when a program or machine responds with human-like behaviors to real world scenarios through its application of contemplation, judgement and intention.

Machine learning is the process of teaching a machine or computer system how to make accurate predictionsor rather, make smart decisionsupon receiving data. Machine learning is a branch of AI based on the concept that machines can identify patterns, learn from past experiences and make decisions with little to no human intervention.

To say that AI and machine learning are the same thing is to say that about a banana and fruit. While a banana is a type of fruit, a banana is not all types of fruit. An apple, a peach and a pear (to name a few) are also fruit, but not a banana.

Same goes for machine learning and AI. Machine learning is a subset of artificial intelligence, while AI also encompasses other branches including neural networks, robotics, expert systems, fuzzy logic and natural language processing.

How is AI going to change QA? Many professionals would argue that it already has. Successful QA teams can already give credit to AI for impacting their QA testing processes, from delivering faster, clearer results to creating easier test cycles.

Dont shift careers just yet. The value that AI brings to the QA testing process is because of its interaction with humans. AI is already impacting companies by enhancing the skills of QA testers and providing instant value to business growth.

Software testing will always require human QA testers, be it for data analysis or for exploratory and regression testing. Yet, we know all too well that even the most skilled QA engineers can make mistakes. The handling of extensive data can be overwhelming to testers, leading to lost focus on software QA and not catching all defects during the testing process.

Thats not the case for development cycles that include AI QA. In fact, testers that apply AI for QA testing receive more accurate results. This is because QA teams execute test cases with AI technology designed to acquire the understanding of source analysis techniques and revisit this knowledge in future instances. Using AI technology for data analysis significantly reducesif not eliminateshuman error while cutting down the time it takes to perform tests and locate defects.

No need for your QA team to feel threatened. With AI taking on the responsibility of executing a variety of test cases, this opens up possibilities for QA testers to acquire new skills and sharpen current competencies. QA engineers who work alongside artificial intelligence will see a boost in their understanding of algorithmic analysis, neuro-linguistic programming and business intelligence.

QA engineers may find their roles quickly changingand for the better. Companies that invest in AI must also invest in their employees to oversee this technology, transforming their position at the business from QA tester to:

As more and more companies integrate AI for QA testing, we can expect to see improvements within customer processes. Fortunately for the IT industry, consumer demands rarely stall. However, consumer demands can always shift and choose the competition, meaning lower brand loyalty, reduced production and less revenue.

AI QA provides insight on future demands more easily through predictive analytics. With the help of AI technology, QA testers conduct data analysis in order to gain insight into consumer purchasing patterns.

Machine learning has come a long way since its early days on the AI scene. Not so long ago, machines relied on developers to continuously feed it a combination of algorithms, formulas, patterns and trends in order to produce results. But this isnt smart behavior, not when the machines didnt analyze data or learn from past experience.

This is no longer the case in machine learning testing. Programmed algorithms are still the foundation of machine learning in software testing, but now machines can evolve based on what theyve learned through previous data interactions.

Today, software testers find value in machine learnings ability to identify predictive patterns within the data. One of the advantages of automated QA testing is that a variety of test cases, particularly back-end processes, can apply test automation in order to expedite the software testing process.

Machine learning in software testing can now be applied to UI analysis, something previously reserved for human testers. Many digital elements remain constant in design and functionality across companies and industries, such as how to filter search results, locate an online shopping cart and submit payments digitally. Because of this, machine learning testing can execute test cases analyzing the looks and behaviors of these elements. Through validation tools, machine learning also can carry out image-based testing to identify visual defects within the software, something almost impossible to pick up through human-led regression testing.

Without machine learning in software testing, minor complications within the code often leads to lengthy tests for QA engineers to perform. Machine learning testing tools can determine the lowest amount of tests required to execute code modifications. Machine learning is designed to provide fast interpretation of data so that QA teams can identify current test coverage within the project as well as vulnerable areas within the software product.

Insight into APIs is difficult without the assistance of machine learning in software testing. Machine learning testing tools offer QA testers a way to carry out check-ups within the API layers. Instead of sending out a multitude of API calls, machine learning can quickly analyze all test scripts through its algorithms.

The future of AI testing is now. Businesses that commit to implementing artificial intelligence and machine learning in software testing today can exceed consumer expectations of tomorrow. Your future success depends on the strength of your technology, so this is your opportunity to be a step ahead of your competition by applying the future of technology to todays testing practices.

Upgrading your QA processes doesnt have to be challenging or intimidating. Companies can partner with a reliable QA services provider like QASource to streamline the process. Our team of testing experts showcase years of experience in AI testing and can help you implement AI QA testing tools and machine learning testing practices within your development cycle.

This publication is for informational purposes only and nothing contained in it should be considered legal advice. We expressly disclaim any warranty or responsibility for damages arising out of this information and encourage you to consult with legal counsel regarding your specific needs. We do not undertake any duty to update previously posted materials.

QA Testing, Software Testing | By Amanda Sturdevant

QASource exists to help organizations like yours enjoy the benefits of a full QA department without the associated setup cost and hassle. With an emphasis on time-bound delivery and customized solutions, we excel at helping our partners manage the quality of their deliverables while keeping costs low.

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