Category: Software Testing Trends

What is Cloud Testing? Benefits, challenges, tools and more!

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Traditional Software testing requires costly and dedicated infrastructure and sources.

Software is growing in complexity which makes it very tough and pricey to build and preserve testing facilities that imitate actual-existence environments in-house.

The maximum consistent issue in software services and products is evolution. This evolution is mainly due to new technologies and person/patron necessities; one such technology is Cloud Computing aka cloud testing.

In cloud testing, Cloud computing environments are used to simulate an actual global state of affairs.

Cloud refers to software programs checking out the usage of assets from the cloud infrastructure this is acquired on-call at a reasonable cost due to the pay-according-to-use nature of cloud computing.

It does not shop facts on private computer systems. It gives on-call the availability of PC offerings like servers, data storage, networking, databases, and so on.

What is cloud computing?

The “cloud” has been used as a metaphor for the Internet browser and an Internet connection Cloud computing is on-demand access, through the net, to computing resources—programs, servers facts Storage, improvement tools, networking skills,

Cloud computing is a way of leveraging the Internet to consume software or other IT offerings on call. Users’ percentage processing electricity, Storage space, bandwidth, reminiscence, and software.

Users will pay as they pass and handiest use what they need at any given time, preserving the price to the consumer. Cloud computing is a business model as well.

Instead of buying and installing the physical infrastructure essential to run software packages, Cloud computing gives a streamlined, simplified strategy to this complexity and the capital expenditure it necessitates.

Cloud computing models.

There are 3 models of Cloud Services:

Saas:  Software as a service is a cloud version that provides programs over the internet as a carrier.

Users no longer to install and hold the software on their devices, they can get access to it through the net, rather than managing the software and hardware with the aid themselves.

It is likewise called a Web-based software program, hosted software.

It runs on SaaS service provider servers and manages software applications, inclusive of security, availability, and overall performance.

Paas: Platform as a Service (PaaS) is a cloud model in which hardware and software assets are provided to users over the net by a third-party supplier.

These resources are required for application development. A PaaS service provider hosts the hardware and software on its infrastructure and developers install hardware and software to run new software applications freely.

Iaas: Infrastructure as a service (IaaS) is a cloud version that provides digital computing sources over the net. A user can get a very new digital system with the desired configurations and the use of IAAS without spending an extra price.

Types of Cloud

  • A public cloud is a service provided by a cloud computing service provider to most people. These services can be availed by any person who desires to use them; all they need to do is pay for the offering’s services.
  • Private cloud, this the infrastructures are provided for the different use of an organization. The company can be the proprietor of the infrastructure, or a 3rd party might also own them.
  • Community cloud, the infrastructures are given for using a particular group of users from organizations that represent the specific community.
  • A hybrid cloud is the combination of the above model.

What is Cloud Testing?

Cloud testing means software testing by the resources from the cloud infrastructure. In this kind of testing, the software program to be examined takes the benefit of cloud infrastructure and technology to simulate an actual global scenario.

Cloud computing has affected all components of the software program existence cycle, including software testing.

Testing as a Service (TaaS) is terminology like SaaS, IaaS, and PaaS in cloud computing. it consists of the usage of the cloud and testing of the cloud.

TaaS is a model in which testing activities are purchased or hired on the customer’s demand from the third party which has all the testing resources for a real-world environment.

This model gains more recognition when technology and customers’ requirements become more complex, and the need for high-quality, error-free, easy-to-use, and flexible software arises.

TaaS has aloof the problem of installing and maintaining the test environment and sourcing of test assistance.

This helps to reduce the costs of software production. Almost all software testing can be completed e on the cloud

Why do you need Cloud testing/ benefits of cloud testing?

  • Cloud testing has become very critical in software program development these days, and the need to perform testing as a service (TaaS) can’t be overemphasized.
  • In conventional software testing, the company desires to have the hardware and software program for testing, and it is very high priced to hold the hardware and software program and also to resume their license.
  • Cloud technology brings support using presenting a way to check the software in a completely challenging and dynamic environment the use of the pay-as-you-use feature of the cloud, cuts the fee of software production.
  • Virtualization, a vital function of cloud generation gives a manner to check software programs in one-of-a-kind surroundings like extraordinary working structures, configuration, and systems.
  • Using cloud testing helps to improve the performance of software program testing by way of decreasing the time to construct the test environment.

Cloud Testing tools.

SOASTA: CloudTest is a production overall performance testing device for Web packages. It can simulate thousands of virtual public cloud infrastructure services.

ITKO LISA: This product is designed to improve the effectiveness of the software program development teams, especially the ones concerned with cloud computing and custom applications

LOADSTORM: This is a tool that is straightforward and much less high-priced. It is used for load checking out of internet-primarily based and mobile applications.

BLAZE METER: This tool is utilized in measuring load checking out and peer-to-peer overall performance of cellular applications, websites, and application programming interfaces.

iGATE PATNI: Two businesses, iGATE and Patni, got into an alliance and gave delivery to one of India’s largest IT businesses, iGATE Patni. This organization also provides a TaaS solution, which is a cloud-based framework for dynamically scalable and occasional-fee take-look at automation.

Types of testing that can be performed in cloud testing:

FUNCTIONAL TESTING

Functional testing is a high-quality warranty check that deals with all of the consumer necessities, affords the capacity to ensure the system works as anticipated, and gives each developer and user a guarantee that the product meets client requirements.

Functional testing of the Internet and non-internet-based programs can be accomplished in the cloud.

NON-FUNCTIONAL TESTING

This testing is also referred to as the performance testing technique; it’s miles executed to make certain that the utility meets the overall performance expectation of the user.

The scope of nonfunctional requirement testing is broader in cloud testing than in conventional testing.

Performance testing

The maximum essential element of performance testing is to investigate the software’s firmness and scalability.

Pace determines whether or not the application responds speedily; firmness determines whether or not the software remains equal under changing conditions and scalability, which determines the best consumer load a system can manage.

Stress checking out and cargo trying out is each variety of performance testing.

Cloud Testing Vs Conventional Testing

Parameters Cloud Testing Conventional Testing
Definition Cloud Testing is one form of software testing wherein the software program packages are examined by way of using cloud computing services. Conventional testing is a sort of testing in which software is examined primarily based on pre-defined testing standards in line with the exceptional management machine to maintain standards.
Test Environment Cloud testing offers test surroundings-based applications as well as on-user and utilization of applications to test as per custom requirements and satisfaction. Conventional testing has a pre-described environment for checking out any software. This testing was performed in a check lab with restrained assets.
Cost of Testing The cost of testing in cloud testing is much less compared to conventional testing as there is no need to maintain physical infrastructure for testing. Users and clients handiest pay what they use. The cost of testing in conventional is better as we need to hold physical infrastructures and software as nicely required for trying out.

 

Benefits of Cloud Testing.

  • Availability of Mandatory testing environment: In cloud testing, the client’s environment can be effortlessly reflected for effective testing without making an investment in the extra hardware and software program resources for testing.
  • Less costly: Cloud testing is extra cost-efficient than conventional types of testing. Customers, and the testing team, pay for the simplest of what they have used.
  • Quicker testing: It is quicker than the traditional method of testing as corporal infrastructure management for testing is removed.
  • Scalability: Cloud computing assets may be expanded and decreased every time required, based on the demands.
  • Customization: Cloud testing may be custom-designed in keeping with the usage, price, and time primarily based on the style of users and user surroundings.
  • Tragedy regaining: It can be done without difficulty viable as the statistics backup is taken on the cloud vendors as well as at the user’s stop.

Challenges of Testing in the Cloud 

Performing Testing periodically

Performing Testing Periodically Test labs in agencies commonly sit idle for a longer duration, consuming capital, electricity, and real property.

Just about 50% of the infrastructure earmarked for testing is underutilized.

As cloud computing is a common environment wherein a single Server with a couple of CPUs, RAM, and SAN or NAS garage are divided among many users via digital hosts or IP addresses.  All users in a cloud computing environment are in the end accessing a physical system.

Therefore, at a time if any person is the usage of the assets intensely then it can create overall performance issues for the alternative customers at the same time.

So, the performance testing results from the team may vary on occasion depending on the load of the cloud environment. This is one of the tests demanding situations in a cloud environment

Data migration

Migration of information from one cloud carrier company to another turns into a tough project understanding the different database schema requires plenty of time.

Integration Concerns:

A distributed application offers many special servers which might be on a web page or off-web site.

In the cloud environs, the dispensed application will deal with many specific digital machines which will be onsite or offsite and there will be response time challenges in the integration of these virtual machines.

The company simply orders the digital machines on the cloud without knowing their bodily locations. However, if machines are placed at a long distance, then latency troubles can occur frequently.

Testing of all components

Performing cloud testing calls for testing all the components associated with an application that needs to be tested just like the server, garage, and network, and also validating them in all layers.

Qualitative Services

Business is frequently concerned with the migration of their critical packages to the cloud environment.

They have the principle situation approximately service availability, scalability, flexibility, and continuity.

Critical commercial enterprise packages are enterprise touchy and can incur a big loss to the agency both in terms of popularity and money if the carrier-level agreements with the patron aren’t met effectively.

Therefore, the cloud computing environment desires to offer demonstrated take a look at outcomes in terms of service availability, scalability, flexibility, and continuity.

Functional Testing aspects

The business application running on the cloud computing surroundings is needed to bypass the behavioral elements of the utility in conjunction with system testing, acceptability testing, integration testing, and interoperability testing.

All of those tests must have been completed before any employer ought to flow its business-crucial application to the cloud computing surroundings.

The testing surroundings ought to resemble the actual cloud environs to obtain the high-fidelity test results.

Operational challenges

Obscure from technical challenges, there are a pair of operational challenges as follows.

Lack of Standardization 

There’s a loss of widespread solutions to integrate the general public cloud with inside data centers. there’s an absence of interoperability.

Hence, it’s miles tougher to switch carriers if the office desires to.

Security Concerns in the Public Cloud 

Data within the cloud is also stored in a remote location that will lie outside the company’s legal reach.

Privacy and Data Security in Cloud packages are multi-tenant in nature, owing to the same the threat of unauthorized data access cannot be ruled out.

The tester has to confirm that there’s no leakage of data while testing the business application over the net in a cloud environment.

Data inside the cloud may be stored in an exceedingly faraway location that would lie out of doors the company’s legal attain.

The tester desires to ensure that there is also no leakage of data at the same time as testing the commercial enterprise application in a cloud environment.

Usage 

Because there are just a few options accessible, improper utilization may occasionally result in expense increases for newer firms. To encourage cost efficiency, businesses should thoroughly assess their demands before choosing a cloud vendor.

Planning 

The assembly, use, and disassembly of the environment should be carefully planned by the project teams and test teams.

To reap the utmost benefits of Cloud, the usage has got to be planned properly.

Performance 

As cloud infrastructure is shared, there are also scenarios where performance dips.

There could also be planned downtimes/maintenance windows on the cloud vendor side which might impact performance too

Accessibility and Recovery Testing

The Other challenges that the testing team could countenance are as follows.

Application data replication testing should be tested with the help of a third-party vendor as replication is not under the control of the tester in the case of the cloud environment.

Lastly, the tester needs to deliver their due diligence to procure accurate test results which should be comparable to the physical server environments.

Environment Configuration

for cloud testing, it becomes hard to manipulate the infrastructures like servers, storage, and so forth. Deployment and testing consequently, lead to troubles.

Use of Multiple-Cloud Models: It will become hard to manage more than one cloud at a time which can lead to complications, security, and synchronization issues.

Upgradation in Cloud: The biggest assignment of cloud testing is to do up-gradation in the cloud and ensure it does not affect the prevailing users and data.

Conclusion

Hope you like the article about what is cloud testing. Traditional software testing is great, but the cloud makes work faster and easier.

However,  relying on the cloud all the time is not a wise decision as well. It’s your requirement that should define the technology.

What is Data Lake? Architecture and Importance

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A data lake is a collection of raw data in the form of blobs or files. It acts as a single store for all the data in an enterprise that can include raw source data, pictorial representations, charts, processed data, and much more.
An advantage with the data lake is that it can contain different forms of data including structure data like a database including rows and columns, semi-structured data in the form of CSV, XML, JSON, etc.
It can also store unstructured data like PDF, emails, and word documents along with images, and videos.
It is a store of all the data and information in an enterprise. The concept of the data lake is catching up fast due to the growing needs of data storage and analysis in all the domains.
Data lake structure
Let us learn more data lakes.
What is Data Lake?
We need to understand what a data mart First for the answer. Datamart can be considered as a repository of summarized data for easy understanding and analysis.
Pentaho CTO James Dixon was the person who first used the term  As per him, a data mart is like packaged and cleaned drinking water that is ready for consumption.
The source of this drinking water is the lake. Hence the term data lake. A storehouse of information from where the data mart can interpret and filter out the data as needed.

What is the importance of the data lake?
it’s a huge storage of raw data. This data can be used in infinite ways to help people in varied positions and roles.
Data is information and power, that can be used to arrive at inferences and help in decision making too.
What is data ingestion?
Data Ingestion
Data Ingestion; what does it does? So well, it permits the connectors to source the data from various data sources and piles them up into the Lake.
What all does Data Ingestion supports?
It supports Structured, Semi-Structured, and Unstructured data. Batch, Real-Time, One-time load, and similar multiple ingestions. Databases, Webservers, Emails, IoT, FTP, and many such data sources.
What is Data Governance
Data governance is an important activity in a data lake that supports the management of availability, usability, integrity, and security of the organizational data.
Factors that are important in Data Lake
Security
Security of data is a must, be it in any kind of data storage, so is true for the data lake. Every layer of the Data Lake should have proper security implemented. Though the main purpose of security is to bar unauthorized users, at the same time it should support various tools that permit you to access data with ease.
Some key features of data lake security are:

  • Accounting,
  • Authentication
  • Data Protection
  • Authorization

Data Quality:
Data quality is another important activity that helps in ensuring that quality data is extracted for quality processes and insights.
Data Discovery
Data Discovery is an activity of identifying connected data assets to make it easy and guide date consumers to discover it
Data Auditing
Data Auditing includes two major tasks:

  1. Tracking changes
  2. Capturing who/when and how the data changes.

It helps in evaluating the risks and compliances.
Data Lineage
Data linkage works on easing error corrections in data analytics
Data Exploration
The beginning of data analysis is data exploration, where the main purpose is to recognize the correct dataset.
Data lake image png
What are the maturity stages of a data lake?
Data Lake maturity Stages
There are different maturity stages of a data lake and their understanding might differ from person to person, but the basic essence remains the same.
Stage 1: in the very first stage the focus is on enhancing the capability of transforming and analyzing data based on business requirements. The businesses find appropriate tools based on their skill set to obtain more data and build analytical applications.
Stage 2: in stage two businesses combine the power of their enterprise data warehouse and the data lake. These both are used together.
Stage 3: in the third stage the motive is to extract as much data as they can. Both enterprise data warehouses and data lake work in unison and play their respective roles in business analytics.
Stage 4: Enterprise capability like Adoption of information lifecycle management capabilities, information governance, and Metadata management is added to the data lake. Only a few businesses reach this stage.
Here are some major areas where data lakes are most helpful:

  • Marketing operations: the data related to consumer buying patterns, consumer purchasing power, product usage, frequency of buying, and more are critical inputs for the marketing operations team. Data lakes help in getting this data within no time.
  • Product Managers: Managers need data to ensure the product delivery is on track, check the resource allocation and their utilization, their billing, and more. Data lakes help them in getting this data instantaneously and at the same place.
  • Sales Operations: while marketing teams use the data to design their marketing plans, the sales team uses a similar set of data to understand more about the sales pattern and target the consumers accordingly.
  • Analysts and data architects: For analyst and architects’ data is there bread and butter, they need data in all forms. They analyze and interpret data from multiple sources in several ways to derive inferences for management and leadership teams.
  • Product Support: Every product once it is rolled out to the consumers will undergo several changes as per the usage patterns. These are taken care of by the product support teams. The long-term data of the enhancements requested and the features most used, the team may decide to roll out more similar or additional features.
  • Customer Support: The teams handling customer support divisions may come across several issues and resolutions day-in and day-out. These issues may get repeated over the months, years and even decades. A consolidated data of these issues and resolutions are very helpful to the executives when they reoccur.

Also Read: All you need to know about Big Data Testing is here!
Advantage of a data lake
In this section let us list out some of the obvious reasons and advantages of having a single repository of data that we call a data lake.

  • One of the biggest advantages of a data lake is that it can derive reports and data by processing innumerable raw data types.
  • It can be used to save both structured and unstructured data like excel sheets and emails. And both these data points can be used for deriving the analysis.
  • It is a store of raw data that can be manipulated multiple times in multiple ways as per the needs of the user.
  • There are several different ways in which one can derive the needed information. There can hundreds and thousands of different queries that can be used to retrieve the information needed by the user.
  • Low cost is another advantage of most of the new technologies that are coming up. They aim to maximize efficiency and reduce costs. This is true for data lakes as well. They provide a low cost, single point storage solution for a company’s data needs.

All data in a data lake is stored in its raw format and is never deleted. A data lake can typically scale several terabytes of data in the original form.

What’s the architecture of a Data lake?
The above image is a pictorial representation of the architecture of the data lake.
ingestion tier – Contains data source. Data is fed to the data lake in batches and that too in real-time
Insights tier – Located on the right side, is the insights of the system.
HDFS – Spcialy built a cost-effective system for structured and unstructured data.
Distillation – Data will be retrieved from storage and will be converted to structured data
Processing – User queries will be run through analytical algorithms to generate structured data
Unified operations – system management, data management, monitoring, workflow management, etc.
Data lake architecture
Differences between data lake, database, and data warehouse
In the simplest form data lake contains structured and unstructured data while both database and data warehouse except pre-processed data only. Here are some differences between all of them.

  • Type of Data: As mentioned above, both the database and the data warehouse need the data to be in some structured format to save. A data lake, on the other hand, can contain and interpret all types of data including structured, semi-structured and even unstructured.
  • Pre-processing: the data in a data warehouse needs to be pre-processed using schema-on-write for the data to be useful for storage and analysis. Similarly, in a database also indexing needs to be done. In the case of a data lake, however, no such pre-processing is needed. It takes data in the raw form and stores for use at a later stage. It is more like post-processing, where the processing happens when the data is requested by the user.
  • Storage Cost: The cost of storage for a database would vary based on the volume of data. A database can be used for small and large amounts of data inputs and accordingly the cost would change. But for when it comes to a data warehouse and data lakes, we are talking about bulk data. In this case, with new technologies like big data and Hadoop coming into picture data lakes do come forth as a cheaper storage option as compared to a data warehouse.
  • Data Security: Data warehouse has been there for quite some time now. Any security leaks are already plugged. But that is not the case with new technologies like data lakes and big data. They are still prone breaches, but they will eventually stabilize to a strong and secure data storage option soon.
  • Usage: A database can be used by everyone. A simple data stored in your excel sheet can also be considered as a database. It has universal acceptance. A data warehouse, on the other hand, is used mostly by big business establishments with a huge amount of data to be stored. While data lakes are most used for scientific data analysis and interpretation.
Data Lake Data Warehouse
Stores everything Stores only business-related data
Lesser control Better control
Can be structured, unstructured and semi-structured In tabular form and structure
Can be a data source to EDW Compliments EDW
Can be used in analytics for the betterment of business Mainly used for data retrieval
Used by scientists Used by business professionals
Low-cost storage expensive

Data Lake Implementation
Data Lake is a heterogeneous collection of data from various sources. There are two parts to any successful implementation of a data lake.
The first part is the source of data. Since the lakes take all forms of data, the source need not have any restriction. This can be the company’s production data to be monitored, emails, reports, and more.

  1. Landing Zone: This is the place where the data first enters the data lake. This is mainly the unstructured and unfiltered data. A certain amount of filtering and tagging happens here. For example, if there are some values that are abnormally high when compared to others, these may be tagged as error-prone and discarded.
  2. Staging Zone: There can be two inputs to the staging zone. One is the filtered data from the landing zone and the second is direct from a source that does not need any filtering. Reviews and comments from the end-users are one example of this type of data.
  3. Analytics Sandbox: this is the place where the analysis is done using algorithms, formulae, etc. to bring up charts, relative numbers, and probability analysis as and when needed by the organization.

Another zone that can be added to this implementation is the data warehouse or a curated data source. This will contain a set of structured data ready for analysis and derivations.

Best practices for Data Lake Implementation:

  • Availability should form the basis of the Designing of Data Lake instead of what is required.
  • The native data type should be supported by Architectural components, their interaction, and identified products.
  • It should support customized management
  • Do not define schema and data requirement until queried for disposable components integrated with service API forms the base for designing.
  • Attune Data Lake architecture to the specific industry. Ensure that the necessary capabilities are already a part of the design. New data should be quickly on-boarding
  • It should also support present enterprise data management techniques and methods
  • Independently manage Data ingestion, discovery, administration, transformation, storage, quality, and visualization

data structure
What are the Challenges of building a Data Lake:
Some of the common challenges of Data Lake are:

  •  Data volume is higher in the data lake, so the process has to be dependent on programmatic administration
  • Sparse, incomplete, volatile data is difficult to deal with.
  • Wider scope of the dataset
  •  Larger data governance & support for the source

Risk of Using Data Lake:
Some of the risks of Data Lake are:

  • A risk involving designing of Data Lake
  • Data Lake might lose its relevance and momentum after some time.
  • Higher storage & compute costs
  • Security and access control.
  • Unstructured Data may lead to unprecedented Chaos, data wastage, Disparate & Complex Tools, Enterprise-Wide Collaboration,
  • No insights from others who have worked with the data

Example for Data Lake
Think about this scenario where unstructured data that you have can be used for endless purposes and insights. However, possession of data late doesn’t implicate that you can load all the unwanted data. You don’t need data swamp right? The collected data must have a log called a catalog. Having data catalogs makes the data lake much more effective.
Examples of Data Lake based system include,

  1. Business intelligence software built by Sisense that can be used for data-driven decision making
  2. Depop , a peer to peer social shopping app used Data lake in Amazon s3 to ease up their processes
  3. A market intelligence agency Similarweb used data lake to understand how customers are interacting with the website

and many more.
Also Read: Wish to know about data-driven testing?
What is Snowflake? 
Snowflake is a combination of a data warehouse and a data lake, taking in the benefits of both.
It is a cloud-based data warehouse that can be used as a service. It can be used as a data lake to give your organization unlimited storage of multiple relational data types in a single system at very reasonable rates.
This is like a modern data lake with advanced features. Being a cloud-based service, the use of snowflakes is catching up fast.
Data Lake solution from AWS
Amazon is one of the leading cloud service providers globally. With the advent and extensive use of the data lakes, they have also come up with their own data lake solution that will automatically configure the core AWS services that will help to simplify tagging, searching and implementing algorithms.
This solution includes a simple user console from where one can easily pull the data and analysis that one needs with ease.
Below is a pictorial representation of the data lake solution from Amazon.
Some of the main components of this solution include the data lake console and CLI, AWS Lambda, Amazon S3, AWS Glue, Amazon DynamoDB, Amazon CloudWatch, Amazon Athena among others.
Amazon S3
Amazon’s simple storage service or Amazon S3 is a web-based object storage service launched by Amazon in March 2006.
It enables organizations of all sizes to store and protect their data at a low cost. As shown in the diagram above, it is a part of the data lake solution provided.
It is designed to provide 99.999999999% durability and is being used by companies across the globe to store their data.
With the growing needs to data and the requirement to make it centrally available for storage and analysis, data lakes fit the bill for most companies.
Newer technologies like Hadoop and big data facilitate the storage and assimilation of huge amounts of data centrally.
There are still some challenges with respect to data lakes and they are likely to be overcome soon, making data lakes the one-stop solution for the data needs of every organization.
 

What is BDD (Behavior Driven Development)? Why is it important?

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TDD is very useful to guarantee a quality code, but it is always possible to go a step further, and that is why the  BDD Behavior Driven Development was born.
Driven development behavior (Behavior Driven Development) uses concepts of DDD (Domain Driven Design) to improve the focus of TDD.
BDD is the answer that Dan North gave to the difficulties presented by TDD. One of the reasons for blocking documented in his article Introducing BDD was the fact of calling the tests “test”.
This leads to the erroneous consideration that the mere fact of conducting tests means that the application is well done.
North introduced the concept of “behavior” to replace the “test”, and the change solved many of the doubts that arose when applying TDD. Soon after, in 2003, it would launch JBehave, the first BDD framework, based on JUnit.

How does BDD work?

The BDD tests are written in an almost natural language, where the keywords that define the process that gives value to the user prevail. A BDD test looks similar to the following:
Scenario:   Add a product to the shopping basket
Given I am viewing the article page
When I click the “add to cart” button
Then   the shopping cart counter increases
And    the item appears in the shopping cart
The keywords in orange are the ones that BDD tools like JBehave, Cucumber or behave interpret.
The test cases are scenarios (Scenario), which have an initial status (Given), one or more actions of our own test (When) and consequences to prove (Then).
If there are more actions of a specific type, we will connect them with an And.

The scenarios are defined in flat text files (features), which are easily readable by all parties.
The concrete implementation of the steps that are defined in these scenarios is done in the steps files, where the programmers are responsible for scheduling the actions that they want to try to perform.

Why BDD?

BDD makes it easier for the developer to determine the scope of their tests; it is no longer about testing methods or classes, but about ensuring that functionality behaves as the user expects.
Another of the main advantages of BDD is the use of language that all the interested parties can understand with minimum training, without having previous knowledge of programming.
Thanks to this, all parties involved in the development of a product can understand what is being worked on and what is the functionality involved.

When a BDD test fails, the entire team is able to identify the component that is failing and can contribute ideas to the conversation, where they all add up.
The BDD also allows designing the product tests around the domain and performing tests that effectively add value to the end user.
The BDD tests know the application the same as the user, and therefore force all the teams to work by functionalities and behaviors of the application, without forcing a concrete organization of the code internally.
Finally, another advantage of BDD is that it allows reusing a large part of the test code, since the common steps of the tests (login, click on a button …) can be standardized and re-used several times.

Why you must use BDD?

Behavior-driven development gets hold of uncertain user stories and acceptance criteria and converts them into a proper set of options as well as examples that may be wont to generate documentation, automatic tests, and a living specification.
In alternative words, it gets everybody on a constant page and ensures that there’s no miscommunication concerning however the software system behaves or what price it provides to the business.
At very least, BDD is value vie nearly any software system project that needs input from stakeholders and business folks.

It’s an excellent thanks to dramatically curtail on the waste that’s generally seen in software system comes whereas guaranteeing that they’re delivered on time and on a budget instead of changing into a data point.
The tests that you simply write also will be loads of additional intelligible and purposeful to everybody on the team.
Deliberate Discovery
Imagine a long software system project that your team recently completed.
However long did it take from origin to delivery? Currently, imagine that you simply may do a constant project over with everything unbroken the same—except your team would grasp everything they learned throughout the initial project.

Also Read : AI and Bots: Are They The Future of Software Testing?

However long would the second project desire complete? The distinction between these 2 eventualities tells you that learning is that the constraint in software system development.
Deliberate discovery is that the initiative in behavior-driven development: Learning as quickly as potential to get rid of the constraints on a project to deliver on time and on budget.
What is Deliberate Discovery?
Most software system development groups’ are acquainted with user stories and acceptance criteria.
As an example, a user story for Twitter could state that a user will reply to a different user’s tweet.
Acceptances criteria facilitate outline the specifics of however that practicality is going to be enforced, like the presence of a reply button on the primary user’s profile.
The matter is that these 2 tools—user stories and acceptance criteria—never explore any unknowns.
The deliberate discovery means having conversations regarding user stories and acceptance criteria victimization concrete examples—and presumptuous content.

As an example, a user would possibly ask: can my reply seem in my Twitter feed for anyone to see? The initial user story and acceptance criteria might not have such the solution thereto question, however, it might clearly have an enormous impact on the design of the general application.
Rather than building software system and golf shot it before of users to induce feedback, the goal of deliberate discovery is to undertake and learn the maximum amount as potential before writing any code to reduce waste and maximize productivity.
Who ought to be involved?
Deliberate discovery processes ought to involve as many alternative team members as you would like to supply insights into their specific areas of experience.
Developers might imagine of options on a really technical level, whereas domain specialists could have insights into what actual customers square measure searching for in terms of practicality.
All of those insights square measure crucial for reducing the uncertainty around options and ultimately meeting the software’s business goals.
Some samples of team members to incorporate are:

  • Domain specialists
  • Business analysts
  • UX designers
  • Users
  • Developers
  • Testers
  • OPS engineers
  • Product house owners

Getting in the correct mental attitude
Team exercises square measure an excellent thanks to getting everybody within the right mental attitude for future deliberate discovery conferences.
Liz Keogh suggests one common exercise that works best in little teams of 3 or four folks in an exceedingly dedicated meeting space with a whiteboard.
The exercise will commence by drawing four columns on a whiteboard:

Also Read : How To Choose The Best Test Management Service

Story Column: all and sundry ought to tell a story a couple of drawbacks they need to be encountered and a discovery they created to resolve the matter.
Commitment Column: What choices were created that solid the problem as an example, choices concerning deadlines or writing the incorrect code?
Deliberate Discovery: may you have got discovered data earlier that may have LED to a unique decision as an example, speech customers or emotional early.
Real choices Column: however may you have got unbroken your choices open longer as an example, creating a commitment later once additional data was on the market?

After finishing the exercise, the team ought to discuss however adding the invention method may facilitate them to establish and avoid the issues.
The takeaway ought to be that creating the invention early usually prevents the matter from happening within the 1st place.
Conclusion
BDD is a powerful tool capable of generating real value for the user by focusing the tests on the final product as a whole and not on the code.
If you decide to take the step and try it, you will see how BDD can be your best ally in software development.

Why Testers Should Focus on Adaptability?

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Apart from your technical knowledge, a lot of soft skills also play an important role in paving a way to your successful software testing career. Adaptability is one of those.
What is adaptability?
Adaptability refers to the skill to change your action plan according to changing conditions.
Adaptability is not only adjusting or changing as per some situation, it includes the ability to bring changes keeping the process running smoothly, and without any key obstacles and delays.

An adaptable person is also defined as:

  • Empathetic
  • Resilient
  • Team player
  • Creative problem solver
  • Open minded
  • Good listener

With the highly dynamic and ever-evolving business, it becomes very important for employees to adapt to the changing demands of this business.
You can have new requirements coming in, or there could be a requirement change or change in the deadlines or an unexpected bug that might require further investigation; all these situations demands you to be very flexible to adapt to new changes.
This adaptability becomes even more important for the testers.
Why is adaptability even more important for testers?
Business scenarios have become very dynamic in the past few decades. Technology, methodology and business environment keeps evolving every now and then. The software field is even more dynamic and evolving, hence it becomes very important for the software testers to be very adaptable to have a stable career.
Here are a few reasons that focus on the importance of adaptability for the testers.
Changing software business models: software business is very dynamic and keeps changing every now and then.
For the past few decades, we have gradually witnessed the software business model changing for products to services. Not only this, there are many other changes that software business has witnessed in the near past.
All these changes ultimately bring a vast change in the working mode of the testers. And hence makes it very important for the testers to be adaptable to these changing business models to let the work progress smoothly without any delays and obstacles.
Changing requirements: Software industry is very prone to changing requirements by the stakeholders.
With every change in their business model, a change in corresponding software is made and this sometimes it becomes an on-going process with multiple requirement changes showing up for the same piece of code.
A tester has to be ready to accept these changes and adapt them to this dynamic requirement changes for delivering his best potential.
Changing technology: Technology these days seems to be changing with a blink of an eye. What was dominant yesterday might not even be an option., the testers need to learn to adapt to new technologies.
There was a time when manual testing was the only option, then came in automatic testing, which became the need of the time and now the automatic testing is gradually being replaced by codeless automatic testing.
To stay in the testing field, the testers have to learn to adapt to these changing technologies.
Varied timelines: The timeliness could be very different for you for the same piece of work. In your last project, you might have completed the same task in 2 days, but for some other project, you might have to complete the same task within a day.
Not even that, in the same project you might have quite lavishly completed the first round of testing, but because of some defect, you might have to rush for the second round. You need to be very adaptable as per the timeliness are concerned.
Dealing with different peers and clients: When in a team, you might have to deal with various different types of peers and your clients might also vary.
Their way of thinking and acting might be very different from one and another. But for you as a good tester, you are required to deal with them equally keeping in mind their nature and knowledge.
You have to adapt to different kind of people you come across in your work.
What are the characteristics of adaptable Testers?
Your characteristics that define that you are adaptable:

  • Intellectual flexibility: you should be capable of assimilating new information to draw a conclusion from it.
  • Being Receptive: you should have a positive attitude towards learning new things to achieve your targets.
  • Creativity: you should always be in a state of experimenting with new things and finding out new ways to deal with challenges.
  • Adapting behavior: you should always be ready to adopt new methods and processes to get better results.


What are the qualities of an adaptable tester?

  • Have to be ready with an alternate solution in case the prior doesn’t seem profitable.
  • should not be scared to take up the responsibility of urgent projects
  • should be ready to explore new roles and responsibilities
  • Must remain poised and calm in difficult situations
  • Have to look out for better options to get maximum profits and best results
  • Should be able to easily to adapt to new ways of working
  • Ought to be flexible when it comes to reallocating their priorities
  • Must possess a positive attitude always.

How to evaluate adaptability of a tester in an interview?
you can test a tester’s adaptability by presenting a question like, how they handled some past situations like how they responded when a long-time process was changed or how they dealt with a difficult peer or a client.
An adaptable tester will not say withering things about others, and will constructively describe both perspectives.
Now, when you know how important it is for a tester to be adaptable, it is time to inculcate adaptability in yourself.
All you need is an open mind and a positive attitude and you will be soon able to adapt to different working scenarios with ease.  Good Luck!

Poor QA Can Be A Thanos Snap For Your Business

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Thanos shook the world with his snap in the 4th Avenger movie Infinity War. He has successfully managed to decimate half of the world with the power of the infinity gauntlet.  Even though it’s a movie, for the first time the world witnessed the victory of a villain in a superhero flick. The same became the main USP for the movie.
However, what Thanos has done can happen to your business in no time if your company is not concerned about Quality Analysis.
The only difference here is there is no End Game to rectify the damage since this is real life.
So what exactly is QA and why is it so crucial? let’s find out!
********End Game Spoiler Alert! ********

Reality Check!
We don’t have a time machine to reverse the damage that has been done. There is no Tony Stark to make it happen, there is no Captain America, Black Widow, Bruce Banner, Ant-man and Hawk-Eye to travel to the past and make it all right.  All we have is the proper methodology and practices to make sure that nothing goes wrong.
Let’s imagine Thanos and his accomplices as potential bugs in any software. They will cause trouble and can be catastrophic to your business.
But you will not get an End Game to correct everything. You have to make sure that you find Thanos eliminate him in the infinity war itself with the help of a team that has the capability of doing so.
The Thanos Snap, Poor QA, How does it affect your business?

  • Low Revenue
  • Losing credibility in public view
  • Increase in production cost
  • Wastage of resources
  • Late product delivery and as a result, poor customer review
  • Reworking cost

Let’s have a look at the most effective way to track bug in any software

  • Always make sure that the process that you are adopting for bug tracking supports the end goal
  • Rely on a tool that suits well with the process
  • Do not throw all at once to your team. Remember they are on a mission make sure that they are focussed and task allocation in such a way that it’s easy on them
  • Your bug tracking database can also work as a scheduling tool for many aspects related to testing
  • Make sure that the defects have been detailed well in the report
  • Learn about multiple bug tracking methodologies and adopt one that you think as effective
  • Time allocated on tasks should be perfect, not up anymore not up any less
  • Do not have vague exit criteria. Make sure that the validation for changes that you have prescribed is satisfactory.

The correct process involved in Quality Analyses

  1. Requirement gathering – Clear idea about the requirement of the project will be written in an understandable format
  2. Test strategy formation – Strategy is essential for efficient QA and to make sure that the stakeholder is confident.
  3. Test planning – Once testers have the basic requirements. Test strategy will be implemented
  4. Test Execution – This is the process where bug and defect tracking and documenting them takes place.
  5. Before release testing – counter checking of implemented changes happens in this phase


Tips from Nick Fury! How to choose the best QA team?

  • Each project requires a unique approach and methodology.
  • Make sure that the personnel involved in the testing has deep-rooted knowledge about the product and methodology that’s about to be adopted for the project

  • Communication skill is very important to make sure that all the testers can communicate well between each other and with you
  • IP (intellectual property protection) is one of the most important aspects of any team.
  • Make sure that the outsourcing company you are relying on will not disclose any detail about your product
  • Make sure that the testers are flexible to various conditions.
  • They should be well-acquainted with various process and methodology of testing and must be able to combine real-life scenarios with product testing.
  • Make sure that the testers can understand the requirements well.

AI and Bots: Are They The Future of Software Testing?

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Software testing has taken an indispensable place with the increasing complexities of the software.
With the rising complexities, new software technologies, ever rising data, software testing is now taking an entirely new dimension. In such a scenario Bots and AI are gradually taking over the manual testing.

But does that means Bots and AI will wipe out testing jobs? Not at all, they will only change how the complete testing process completes.
The information and knowledge regarding AI and bots is quickly increasing with the growing usage of robotics and AI.
And gradually they are taking over the manual efforts in many fields including software testing.
And it is because robotics and artificial intelligence are cost-proficient, easy to use, and time-productive.
When it comes to machine learning in software testing and developments, bots can be much more easily and quickly trained compared to people.
Bots and AI involvement in Software Testing and Development
Bots and AI are predicted to rule the software testing world soon. They have affected the proficiency of software development and testing in many terms.

  1. Testing Scope and Workloads

It is a common practice to add new features to the software. As new features are introduced, the new code is added.
This code further requires software testing to ensure proper working.
This testing sometimes requires creating and running a new set of test cases and sometimes it even demands a rerun of the existing test cases to ensure the new functionality has not altered or affected the existing functionality.

This adds up to the workload of testers and also increases the testing scope.
AI robot can easily recreate the tests to integrate new parameters and can also run parallel tests without adding the workload to the tester.

  1. Debugging Capability

AI and bots can tirelessly work for 24 by 7. They are great when the test cases list is long, or when testing on disturbed systems, etc.
In short, they are very viable on running time-consuming test cases, which would otherwise be tiring for the testers.
They viably expand the time for which test cases can be run without requiring human intervention.
It reduces human efforts by running test cases without human efforts and letting the testers to only inspect the test results and resolve the issues if any.

  1. Advanced Continuous Testing

Continuous testing can enhance the quality of your software. Continuous testing helps report abnormalities clean-up infested information.
But carrying out repeated testing is not a viable option for human testers.
But this task can be very well carried out by bots and AI, resulting in enhanced software quality.

Now vs. Future of AI and bots in Software Testing.
Currently, AI and bots are confined to search for defects only in dedicated parts.
They are not yet tuned to go beyond it and test for bugs in any newly added component.
In the future with more advanced bots and AI, it might discover changes in even the minimal changes in the system.
It would interpret the client’s expectations and produce numerous test cases based on it in minimal time, much faster than human efforts. Currently, AI capability in this aspect is quite limited.
Though we can’t predict what actually AI holds, its capability will definitely see a rise.

Know More: Quality Assurance VS Quality control

The AI and bots can go far beyond our expectations and can take software testing to a completely new standard. What that cannot be expected today might be a reality of tomorrow.
AI and bots have a significant role in the current scenario of software testing.
They have definitely made testing much easier, and quicker. But with advancements made in the field of AI and bots, we will soon see AI and bots taking over the complete software testing, with human intervention required only in managing these bots.

Conclusion:
AI and bots have amazed us enough with their amazing capabilities. But what they still have in store for us cannot be predicted at the moment.
The time is not far away when AI and bots will take over all the manual efforts in software testing. Software testing will soon be easier, cost-efficient and time-efficient using AI and bots.

8 Website Testing Trends of 2020 You Need To Know!

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Websites are turning more complex and UX (user experience) oriented. owing to the same website testing trends has to be done to make sure that all the features and aesthetics are working well.
Apart from that, there are gazillions things that need to be tested to make sure that it will be stable.
Coming back to about testing as a process, will it be the same as that of 2020 when it comes to website testing?
if not, what will be major changes in Website Testing?
1.    Automated mode of testing for more reliability
The test automation provides immense support to the testing team so that they are able to focus their time and imbibe their efforts on creating test cases instead of just managing the tests demands.
It not only ensures the tracking of testing needs but also managing all the testing needs required in ensuring the 100% functionality of application and software.
Test automation also requires to conduct various types of testing along with the test coverage.

It embarks to ensure that a high- quality software is delivered in the market which provides great help to its users.
To control the execution of tests which are used to compare the actual results with the expected results, specialized tools are used.
The actions are repeated which can be automated. However, it will require the regression type of testing.
Both functional and non-functional testing is conducted with the usage of the automation tools.
2.  The testing mode with machine learning
Machine learning is an innovative method to bring about great revolutionary changes in the workflows and processes. While testing software or an application, the machine learning process can be used for following:

  • Test suite optimization – To predict the number of redundant and the unique test cases if any in the software.
  • Predictive analytics – To identify the main objectives of software and application testing on the basis of any historical data available.
  • Log analytics – Evaluation of the tests cases which require the need to be executed and implemented automatically.
  • Traceability – Taking out the main keywords from the Requirements Traceability Matrix (RTM) to attain the test coverage of the software.
  • Defect analytics – To evaluate and working on the high-risk areas of the application and the software for the prioritization and correct optimization of the regression test cases.

3. The changing of the testing world with the addition of the Internet of Things
There are various connected devices than ever before as IoT (Internet of Things) technology is gaining attraction.
The devices based on Internet of Things technology tested during the conduction of Internet of Things testing. Internet of Things systems are tested through the various types of testing as follows:

  • Usability Testing – It keeps a check on the usability of an Internet of Things system.
  • Compatibility Testing – To ensure the compatibility assurance of devices in the whole IoT system.
  • Reliability & Scalability Testing – Virtualization tools which are utilizing the simulation of sensors are checked.
  • Data Integrity Testing – To validate and check the integrity of data present on the Internet of Things systems.
  • Security testing – To ensure the user authentication process and keep a check on the data security controls offered on the IoT systems.
  • Performance Testing – To check the performance of all the connected devices in an Internet of Things system.

4.   Save the time with the help of Shortening Delivery Cycle
Rapid changes in technologies, platforms, and devices are putting pressure on software development teams to deliver the finished products faster and more frequently.
Testing needs to be integrated with development to facilitate delivery.

Also, Read: 10 Effective E-commerce Website Testing Techniques

Even the Software companies are readily investing. Their main aim is the development and delivery processes by employing the right set of tools.
The main reason behind this is their requirement for test management tools. This will help in enhancing shortening delivery cycles match-up.
5. Integration mode with an amazing combination of testing and tools
In 2019, the integration process will definitely disparate test environment which can be used for the facilitation of smart testing which often leads to a sudden requirement of integrated tools and further helps in the requirement of adequate management, task management, bug tracking, and test management.
The rapid increase in the need of integration of elements of a product development due to the shortening of the delivery cycles these days.
The data is further collected or gathered from disparate sources such as requirement management systems, change control systems and task management systems.
6.   Checking on the transformation with Performance Engineering
In 2019 the performance engineering will be easily replaced with Performance testing. It is going to be a new addition in the world of analyzing a software.
In addition to this, the analyzing and execution of all the elements of the system which work together will be the major focus in the forthcoming year.
It works fine in the place of executing performance test scripts. There are major changes that can be seen in terms of performance, security, usability, hardware, software, configuration, business value, and the customer are the various elements present in the system.
The main motive of performance engineering as per their uppermost value the objects are integrated and collaborated and then are ensured for delivering easily and quickly of these items to ensure a high-quality product is the motive of performance engineering.
In 2019, it will be ensured that performance engineering proves to be a great help in satisfaction of the increasing customer expectations.
7.  Combining a different mode of Manual and Automated Testing
The manual and automatically are combined together to work on testing approaches.
They are used by more and more quality assurance professionals in order to avoid the benefits of both and also to overcome their shortcomings if any.

The manual testing is still a prominent method applied in the testing industry.
Automated testing surely brings efficiency in the testing process but still, there are various fields such as usability and design in which manual efforts are required and thus manual efforts prove to be a bonus point for the testing industry.
8.   Big Data joins hands with Testing
Big Data is the term used for the data quality which is generated at a high velocity.
In Big Data testing, testers verify that whether the terabytes of data are successfully processed with the usage of the commodity cluster and other supportive components for the efficient and smooth working of the application or software.
Big Data Testing embarks a great focus on performance testing as well as the functional testing of the application and software.

Read also : Strategy and Methodology of Big Data Testing

In big data testing, has become one of the major factors. It works on the quality of data which is analyzed continuously.
Before commencing the testing process the quality of data is verified. There are various characteristics on the basis of which the data quality is checked such as conformity, accuracy, consistency, validity, duplication, data completeness, etc.
After the completion of all the testing’s an application or the software is said to be error-free and ready to be used.
Bonus point
The immense pressure of digital transformation in today’s business from the very time since the data has become beneficial in increasing insights.
The methodology related to agile that is the trend to adopt the digital transformation is the latest addition to overcome the difficulties in software testing.
It provides an alignment in the digital transformation initiatives received in accordance with the business requirements in the market.
The business challenges, their objectives and the use of cases are well defined by the agile team. In this agile approach, the new adaptive measures are incrementally delivered with each sprint.
As in today’s era, the digital transformation is a continuous or a never-ending process, the agile team helps to render real valuable outcomes easily for the business need rather than standing in a queue for such a long time.
Conclusion
To stay ahead and at the top of the global market, the professionals’ require the need to stay updated and grasp information about the latest testing trends.
The testing of the software and application gives satisfaction to its users assuring that it is correctly informative and also user –friendly.

It helps in the identification and fixing of the bugs in the application and software and the best method to keep yourself immune to the disruptions and bugs in the software industry is to be futuristic.
The software trends mentioned above will help the testers in investing their time and efforts in acquiring and implementing the rights skills and tools for 2020.

Software Testing Trends 2020 – 2021: What To Expect?

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There are numerous perspectives on software testing. It doesn’t generally include utilizing the product. It isn’t just about detecting bugs.  Software testing trends are changing.
Testing can begin around the prerequisites phase. Contemplating what the product ought to do, where threats could be, and how the customer/user explores the product is all part of testing.
You can feel the change is already in the air. Numerous organizations are beginning with Agile, DevOps, Scrum, and Continuous Delivery.
Everybody is centered on conveying new business values actively.
The product can be developed and released significantly snappier than in previous years. This highly affects outdated testing systems and vocation.
Testing and QA experts have to be ahead of evolving patterns.
Let’s see where the software testing industry is going in the coming year (2019) to remain one level ahead.
1. New goals of the tester
Till now the goal of the tester was, for the most part, observed as preventing bugs from being conveyed with the software, he/she will perceive how the benefit of testing begins streaming up in the development procedure, and how the greatest value of the tester in the coming year will be to offer tools like risk assessments, tests, etc. that guarantee not just achieving durability for a few weeks before launching but that the software is steady constantly.
Testers will focus more on automation that helps engineers continually test their progressions.

Additionally, by cooperating with the developers, detecting the bugs they are inscribing into the code as they do it, rather than testing the program and detailing them a couple of weeks or months after the code was written.
2. Fast adoption of  Agile and DevOps
Both practices Agile and DevOps have emerged as a favorite for many organizations. Reason? both the methodologies are designed to favor swift deployment and healthy collaboration between testers and developers.
Agile is a continuous process of testing and development and on the other hand, DevOps is renowned for collaboration of cross departments.
Owing to this, DevOps and Agile facilitate quality products at a faster rate and many companies will adopt the methods in the imminent future.
3. Artificial Intelligence and Machine Learning Testing
Artificial Intelligence gets a huge number of modern advancements in the data technology area.
Trending terms like Machine Learning, Natural Language Processing, Neural Systems, and so on have already become the overwhelming focus.
The best part is frameworks are building more frameworks.
In the banking sector, you could utilize AI and Machine Learning methods to show continuous transactions, and in addition predictive models of exchanges as per their probability of being deceitful.
Companies are trying to drive digital technology with this emerging trend.
With such precedents, a software tester should comprehend the product testing trends of these innovations and describe challenging test situations to find out and approve the results.
Additionally, testing such frameworks will require developing systems that test themselves.
Henceforth, it is recursive test management.
4. It’s about Performance Engineering, Not Performance Testing
Performance engineering will supplant performance testing in the coming year.
As opposed to executing performance test scripts, the attention will be on breaking down how each of the components of the framework cooperates with each other.
The different components of the framework incorporate security, performance, usability, software, hardware, business value, configuration, and user.
The performance engineering is tied in with teaming up and emphasizing the elements of the most noteworthy esteem and conveying them swiftly to guarantee an excellent product.
Thus, performance engineering will help in surpassing customer desires in the year 2019
5. From Traditional to Test Automation
Now testing is not just about performing tasks in those old traditional ways.
Specific tools are utilized to manage the performance of tests to think about the genuine outcomes against the supposed outcomes.
Primarily the regression tests which need tedious activities are automated.
Thus, automation tools will be utilized for both non-functional and functional testing by testers in 2019.
The test automation encourages the testing group to concentrate their time and endeavors on making experiments as opposed to handling testing needs.
Test automation tracks and deal with every testing need, the kinds of testing needed alongside the test inclusion.
Test automation guarantees excellent software is conveyed.
6. Growing Selection of Open Source Tools
Open source tools are extremely helpful for business and will have a crucial role in the year 2019 as well.
There are numerous perks of utilizing open source tools, one of which is no expense, as they’re free resources and have access to all.
Other than this, it is effortlessly customizable, is more adaptable than some costly exclusive products.
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Clients do take part in designing it, so, it truly gives you the opportunity to plan the manner in which you need and there are various integrations for your ground-breaking Test Automation as well.
A controversial point could be that of security, as being accessible to everyone doesn’t give a sense of security, yet when it experiences more usages the odds of discovering the bug and settling it increments.
7. Internet of Things
Internet of Things (IoT) is one of the quickest developing innovations in this era and IoT is a challenge for Test Automation.
An entire set of data online is associated with one another through the web.
The hardware is controlled by a devoted program that associates them to the web and from that point, it interfaces with every other thing.
As awesome as it might sound, there are various vulnerabilities in the framework.
Consequently, the programs or products which are associated ought to be tested for quality, functionality, and most significant security in 2019.
 8. App penetration testing will increase tremendously
A recent study revealed that app development companies have broadened penetration test and are adamant in finding out the loopholes before their product hits the market
The frequency of penetration testing when it comes to business-critical applications has increased a lot, say 4 times a year. Not just web applications, companies are also pen-testing associated APIs, microservices, and back-end enterprise applications.
In the years to come, pen-testing would not be limited to simple testing of internal and external penetration testing. The idea behind the process will be far beyond what’s people are doing right now.
Testing Budgets will keep on Developing
It is nevertheless clear that with such enormous concentration and interest for amazing, high-grade products, and with significant IT patterns, for example, BigData Analytics, AI, Mobility, Virtualization, and Cloud Technologies, software testing has turned out to be something other than just a requirement.
This will push the companies towards dispensing a greater part of their IT financial plan for software testing and QA budget.
Eventually, the Role of the Tester is Evolving in the Coming Year
The greatest chance for the software testers without bounds will be the job they’ll have as a part of the procedure:

  • Who they will serve?
  • What advantage do they give?
  • How would they cooperate with the other software teams?

Every one of these viewpoints will change later on to make the software development process quicker and to finish the product and deliverable quicker.
The single way to be resistant to the interruptions in the software world is to get ready for the future.
Thus, to be ahead of the trend, the software testers have to stay updated on the transforming testing curves.
The above-mentioned trends will encourage testers in advancing their efforts by obtaining the proper skills and tools for 2019.

Also Read : Top 10 Mobile App Testing Companies In The USA

Game Testing: Prominent Trends Of The Future

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For a developer coming up with ideas and developing a game, the product requires creating the perfect balance between the applications of complex software and meeting high expectations of the customers. The end product of creating a game is ultimately expected to offer a certain degree of unsaid anticipation of fun and excitement.

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The game itself, however, may be prone to bugs and defects as the end product depends on the application software that may be accompanied by a share of limitations and complications of its own. Those who are into game development are conscious of and take into account all these challenges before they strive to develop error-free games that not only look and feel great but also work and function equally well.

The availability of game testing services offers game development studios a facilitator that ensure through testing of game functionalities so that they can create robust, error-free, and entertaining interactive application software.

Importance of game testing

The importance of game testing cannot be stressed enough. Games offer an interactive experience which needs to be seamless and enjoyable keeping in mind the complexity with which many entertainment genres are mixed. Video games by themselves are a highly interactive medium which is its biggest selling point. When this is combined with a large user base, with the availability of release of the same game on different platforms such as PC, PlayStation [1], and XBOX [2] it can end up in a lot of unanticipated and unforeseen issues. Without proper testing, such problems can carelessly be overlooked. If end users are delivered with a game product with bugs, it will invite criticism from them as it messes with the seamless experience they look forward to, which can, in turn, lead to the reduction in unit sales.

mobile app testing services banner

Many of the concepts used in conventional software testing are also applicable to game testing saving a few customizations. The focus is more significant in this case on the intangible ‘fun’ aspect that is absent in conventional software testing. There is also a need to take into account various player demographics and educational elements for this.

Below we discuss the types of testing that are relevant to game testing:
Functionality Testing

It refers to detecting any bugs, defects or errors in a game so that it doesn’t unfavourable impact the end-user experience. Some of the things that game testers keep in mind while testing the interactive applications include performance issues such as crashing, freezing and progression blockages. The inspectors thoroughly look at the entire map of the game to identify gameplay issues, graphics problem, and audio-visual issues.

Installation Testing

It is usually done to check and verify the smooth functioning of the game on different platform configurations. We need to ensure that the game can be installed, loaded and run primarily on various platforms that mainstream users support. These are usually covered in test cases.

Feature Testing

This testing is done for the verification of the smooth functioning of the game’s features. These are described in test cases. For covering distinct features, detailed test cases are more suitable. Task-based test cases cater to a superior level of detail. When there are a variety of features for different player statuses, test matrixes becomes handy and more useful.

User Interface (UI) Testing

UI testing aims at focusing on two important things, both the graphical elements as well as the content types. It makes the most sense first to create and develop a relevant list of preferred checks and then work based on this list. The localization of the game should also be covered.

Performance Testing

The first step to performance testing is the need to identify the most common tasks a player is anticipated to perform. Next, the acceptable times for these tasks need to be determined and these times mark the goals. Each of these tasks then needs to be completed smoothly. In some cases, extreme testing is also done by running the game continuously for 24 hours. For testing multiplayer performance, you can increasingly add more players.

The Future

The growth and evolvement of technology is a continuous process in this day and age, and most often the games industry is the front-runner in the technological growth. The game development gives a push for the technological innovation arena.

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The hardware market is driven by the games industry, whereas with conventional software you can see it conforming to the hardware market. The present market trends point towards the growth and demand for VR. Virtual Reality is considered to be the next big thing in the technological growth, especially when it comes to gaming.

VR is anticipated to be a massive scene changes in the gaming industry, giving gamers and end-users a lifelike experience like never before.

We can already see all market segments are covered, right from card box viewers that use a phone as the screen like the Google cardboard project [3], and Samsung with its Gear VR [4]. Even certain gadgets have specific optics to set interpupillary distance, etc. like the Oculus Rift [5], Morpheus [7] and Vive [6].

Companies starting their endeavor in this new area of gaming will try to come up with new methodologies and ways of testing different kinds of hardware and software applications.

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It will ultimately pave the way for new enhancements in the testing. Questions are likely to arise when we think about if we need to immerse ourselves in the game or application before testing.

Only the future can answer our doubts and questions. As far as what awaits the game industry in testing and development is an exciting future that lies ahead where they both go hand-in-hand to create a new level of experience.

Software Testing: What Future Holds?

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We wonder why it took us so long to write on this topic, maybe we wanted some time to let our theories brew.  As the year’s progress, software testing industry is seeing greener pastures. This rapid development in the industry has kept everyone on a hook, especially the testers, expecting them to continuously upgrade their skills.

Software testing plays an important role In the Software Development life cycle (SDLC) which helps improve the quality and performance of the systems. With the growing importance, many big software companies tend to start their testing activities right from the start of the development activities.

Many experts believe that by 2020, software testing will not just be limited to delivering the software without bugs, but will be a huge focus and demand for high-quality products. That’s because software testing is rapidly becoming a standard, rather than a more advanced approach for software development teams.

Below we list some of the top trends in this field for an exception 2018 experience for your tests.

1. Open Source Tools

Most of the software companies use and accept the open source tools to meet their testing requirements.  There are several tools available in the market today, but we can see advanced versions of it ready to be used soon in the near future.  Also, many of the tools like Selenium will jump in the world of AI (Artificial Intelligence) automating most of your testing needs.

2. BigData Testing

Companies today are sitting on top of a huge data repository and all these needs a very strong strategy around the BigData testing. Though BigData testing is difficult than any other testing, the advantages it offers cannot be ignored. The industry has faced many challenges- lack of resources, time and tools, but it has also found its way out of these challenges.

Also Read : All You Need To Know About Software Performance Testing

3. Performance Engineering

The success of software depends upon the performance, reliability, and scalability of the system with user experience as a prime factor. Any software system is incomplete without an interactive user interface. Increased demand for user experience shifts the focus of performance testing to performance engineering.

4. DevOps Integration

DevOps is a concept where the various teams/departments of an IT Organization work seamlessly in collaboration and integration for a project. Since testing plays a very crucial role in SDLC, they are a key person in the business and the overall quality engineering aspects. DevOps is, therefore, a propelling business towards the deployment speed.

5. SDET Professionals

SDET stands for Software Development Engineer in Test (or Software Design Engineer in Test). The concept was proposed by Microsoft and many organizations demand these professionals. The roles of SDET professionals are different from those of our regular testers.  It is said that by 2020, almost all the testers will have to wear the SDET hat to enhance their skills in the testing industry.

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Conclusion:

With the growing needs and changing requirements, software testing professionals need to improve their skills simultaneously. It is not only a challenge for the testing team, but also for the entire development team for addressing the advancements and technological updated. But we are sure the testing industry will knock down these challenges too with their innovations and research.