How Big Data can be leveraged in all aspects of Testing?

Newest data technologies are entering the market; however, the old ones are also going strong. But it is also important to note here that the Big Data technologies adoption will never slow down, not in the near future. So, it is significant to note how we can overcome the challenges that come with this technology. Such technologies have gone beyond the realms of merely being a buzzword. It is now immensely adopted among corporate and companies, irrespective of size. As per the well-known research and advisory firm, “Big Data is high-velocity, high-volume, and/or high variety information assets that demand cost-effective, innovative forms of information processing that enable improved decision making, great insight, and process automation.” It truly forms the basis for machine learning & predictive analytics which is the best thing considering the very real occurrence of networks and enterprises being inundated with data from mobile apps, social, and IoT that produce a 24×7 constant flow of data.

Big Data has a variety of features. The three significant features of big data are:

  • Different types of data
  • Big volume of data
  • Speed and accuracy at which the data can be processed

What is Big Data Testing?

In simple language, Big Data refers to enormous quantities of data. There is no specific size parameter to define the size of this technology. It is safe and sound to assume that the standard method to measure it when comes in petabytes or terabytes. Data comes in from every direction, and the velocity and volume would be monstrous. Software developers will be able to build better software faster, using Machine Learning and Artificial Intelligence technologies like deep learning & natural language processing.

Benefits of Using Big Data Testing in All Aspects of Testing
Through Big Data testing, you can make sure the data in hand is accurate, healthy, and qualitative. The data you had collected from the variety of channels and sources are validated, assisting in better decision making. There are a number of benefits to Big Data testing.

  • Data Accuracy — One of the popular research & advisory firm speaks that data volume is likely to expand by 800% in the next 4-5 years, and 80% of this data will be unstructured. Just imagine the volume of data that you have to analyze. You need to convert all this data into a structured arrangement before it can be mined. Armed with the right kind of data, companies can focus on their weak areas, and be prepared to hit the competition.
  • Better Profit and Reduced Loss — Loss in business will be minimal or even a thing of past, if data is appropriately analyzed. If the accumulated data is of lower quality, the company suffers terrible losses. Separate precious data from structured as well as semi-structured information so no faults are made when dealing with clients.
  • Good Decision Making — When data gets in the hands of the appropriate people, it becomes an asset. So when you have the appropriate kind of data in hand, it would help you make smart decisions. It allows you to analyze all the threats and make use of only the data that will put into the decision making process.
  • Right Strategy and improved Market Goals — You can chart a better decision making plan or automate the decision-making procedure with the assistance of big data. Collect all the validated data, examine it, understand user behavior and make sure all of them are realized in the software testing procedure, so you can deal out something they need. Big data testing assists you optimize business strategies by looking at this information.

Conclusion

Transforming data with intelligence is a great concern. As big data is very important to a company’s decision-making plan, it is not even possible to begin asserting the worth of arming yourself with reliable information. Big Data processing is a very promising field in today’s complicated business environment. Applying the accurate dose of test strategies, and following best practices would help ensure qualitative software testing. The idea is to recognize and recognize the defects in the primary stages of testing and fix them. This helps in cost reduction and better outcomes. Through this procedure, the troubles that testers faced during software testing are all solved now as the testing approach are all driven by data. Hence, Big Data is a powerful tool for the current business. In fact, some believe that companies that fail to derive value from machine learning & Big Data will face huge competitive shortcomings as the field continues to grow and adoption increases around the world.

Security Testing – Critical Concepts and Attributes

The widespread use and high buzz of software apps in business and everyday life are paralleled by the rise of hacking, security breaches, and virus attacks. Behavioral imperfections and software defects can promote these serious attacks. Some of the security incidents like Apple gotofail flaw, Heartbleed, POODLE attack have taught us that web security can’t be taken lightly and even the best of us are not safe and risk-free from it. Third parties with malicious intent may exploit these vulnerabilities for their own profit. Companies may incur a serious loss of legal and security complications, customer trust, terrible slowdown of business operations and high costs of rectification, as a result. Applications Security testing is a critical QA step for businesses to safeguard their software applications. By testing the application for potential security threats and vulnerabilities, potential external attacks may be pre-empted.

Prime objectives of Security Testing

The objectives of security testing can be:

  • To make certain that the adequate attention is provided to recognize the security risks
  • To confirm the proper functioning of the executed security measures
  • To get confirmation that a realistic mechanism to define and enforce access to the system is in the right place
  • To make sure that adequate expertise exists to perform security testing

Usually, security testing has the following main attributes:

  • Authorization
  • Authentication
  • Confidentiality
  • Availability
  • Non-repudiation
  • Resilience
  • Integrity

Why Security Testing?

System testing, in the modern era, is a must to determine and address web application security vulnerabilities and threats to avoid any of the following:

  • Loss of client trust.
  • Website downtime, time loss &expenditures to recover from damage (restoring backups, reinstalling services, etc.)
  • Disturbance to the online means of revenue collection/generation.
  • Cost associated with securing web apps against future attacks.
  • Connected legal implications and fees to have lax security measures in place.

The main aim of security testing is to find out how vulnerable a system may be and to find out whether its data, as well as resources, are secured from potential intruders. The security testing is mainly carried out to make sure that the software under test is sufficiently robust and performs in an acceptable manner even in the event of a malicious attack.

Increasing Importance of Quality Engineering in Software Testing

How can a company win? One of the key criteria is to ensure good quality of its products and services. But the traditional testing and QC paradigm is not enough in the context of emerging technologies. It has proven to be inefficient: if some shortcomings are revealed, the product may have to be redesigned, requiring additional expenses and extra time. That is why something new is being executed in business — quality engineering solutions. Quality Engineering (QE) is the series of procedures by which software quality is analyzed and improved throughout the application or software development lifecycle. It differs from traditional Quality Assurance in that it prevents defects as well as discovers them.

The QE approach implies that every single stage of the product/ software development cycle is under a scrupulous test of quality engineers. Furthermore, the quality maintenance is offered long after the product is delivered. The execution o such strategy in manufacturing or software development procedures guarantees the sufficiency of the output from the very start reduces imperfections, flaws, and reduces potential losses. In other words, quality engineering is the analysis, development, management, and maintenance of diverse systems compliant with high standards.

What are the rewards of Quality Engineering?

With Quality Engineering, the core benefit for your application development cycle is that you are actually making all the proposed advantages of DevOps and Agile more real. Also the teamwork between developers and testers is more real, more in line with the agile ethos. It is also integrated with Test Management solutions so that the outcomes appear on the dashboard instantly, without a human trigger. With shortened release cycles, time to ensure Quality also reduces considerably. Testers have to be involved at the start of the cycle as they will be setting up the testing environment and framework which will be relied upon for all future sprints. Done right, Quality Engineering offers a great deal more speed in testing. It mainly relies more on Test Automation than manual testing. It is hard to imagine a Quality Engineering function that doesn’t have Test Automation at its center. Yet again, done right, it creates more flexibility and speed for the whole development cycle. It is not considered just functional and non-functional testing, but every single layer and integration that can and should be tested.

In current Digital era, a Quality Engineer should have experience in programming and be supposed to be able to write software as the situations demands. While the Software Development team focuses on constantly upgrading the application, the Quality Engineering team main responsibilities are:

  • Setting up new parameters and standards
  • Optimization of test cases, & improving automation efficiency
  • Identification of drawbacks
  • Generating a plan for improvement
  • Plan execution using different tools and methods
  • Assessing & implementing new technologies and tools
  • Following up to make sure that issues have been solved
  • Creating tailor automation solutions to address application specific use cases
  • Create frameworks & accelerators that help scale QE across manifold channels, Enterprise wide.

Quality engineering is driven by emerging technologies like AI (artificial intelligence), Big Data analytics and IoT. Automation is the driving force behind turning the traditional testing into an effectual quality support model.

Bottom line

Performance of the application/ software is of paramount importance. Every outage, crash, drawbacks, and even slowing down of the app or processing/ working on a client request has the potential to impact revenue directly. It is the responsibility of QE team to not only identify such issues, but also work on identifying/removing the root cause of such problems. This demands a sound understanding of app architecture, monitoring tools, several enterprise sub systems that are catering to the app etc. Overall, Quality Engineering team provides substantial insights about the root cause or issue and solved it in the fastest possible manner.

Cloud Computing Trends to Craft in 2019

Ever since its inception in 2000, the Cloud computing has been a buzz topic in the business world and has also proved itself to be the cherry on the cake in terms of digital data processing & storage. Cloud computing is now no longer just a tool, it has advanced as a scalable service offering and a delivery platform in the computing services field. Organizations these days have shifted their focal point towards discovering the appropriate procedures to handle and deal with cloud computing to accomplish their business goals. Along with several value-added services, cloud technologies have emerged from personal cloud storage to a genuine and secure data storage network for the enterprise regardless of its size. So, what’s ahead to 2019 and all important future years? Let us take a look at fresh trending cloud computing attributes of 2019.

Cloud Computing Trends 2019

In the near future specialists foretell that the cloud technologies are more likely to be a utility; this is because approximately every single app that runs on any platform will necessitate storage. In addition, with the advent of IoT, the current era in cloud storage is expected to emerge in the upcoming year. So, let’s take a brief look at “New Cloud Computing Trends in 2019.”

  • Trend of Cloud Storage Capacity

    Get into Cloud Computing if you need Hassle free Storage. Cloud will become an inevitable division of management and operation for almost all enterprises. Software as a Service (SaaS) has already created its trademark in the year 2017 and is now all set to open a flexible and financially smart door for companies and consumers to try early cloud services.

  • Blend of IoT, and Artificial Intelligence with Cloud

    In 2017, the introduction of IoT with its Artificial Intelligence was the talk of the town, and it emerged as the buzzing topic for the business application enthusiasts. The constant progression of IoT with real-time analytics and cloud computing has taken Artificial Intelligence to a new level and has even made it likely for IoT to make things easier and simpler technological interactions at different levels. With IoT spearheading at a fast pace, it is no doubt that it will be at the forefront of technology innovations in 2019 too.

  • Security Becomes Critical

    As we all are aware of the fact that technology has two ways to it. In one side it will give us real power to change the world while in the flip side it always remains prone to the security breach. The advantages of Cloud are trumpeted around the globe and for most enterprises; it has become the simpler decision to embrace Cloud. With the introduction of the GDPR – General Data Protection Regulation, the stress on security concerns has gone even higher. As early as 2019, small enterprises could find themselves threatened with the possibilities of data-theft and an inability to sustain with the General Data Protection Regulation’s rules.

  • Quantum Computing – Closer Look

    We have been discussing quantum computing for decades but in recent times. In reality, as the race for cloud supremacy heats up, we are getting pretty close to realize the dream. All the leading tech giant companies are working around the clock for building the first quantum computer. If they attain that, we will soon make better financial models, solve intricate medical issues, and even we have human-like communications with artificial intelligence. Last November, we took a glance at the fastest available quantum computing platform globally with IBM’s 20-qubit cloud computer. The year 2019 also promises to deliver a whole lot more within the quantum computing space that can modernize cloud services and solutions.

  • Hybrid Based Cloud Services Are on the Hype

    The major complaint about the business owners have when moving their hosting & computing services to the cloud is that it is really difficult. The hybrid cloud can be a blend of on-premise, third-party private cloud, and public cloud services. This system allows workloads to move flawlessly from private to public clouds with no difficulty. With the implementation of the use of a hybrid cloud, enterprises can enjoy a higher degree of flexibility & a range of data deployment options.

Impact of cloud technologies has been global, yet fewer than half companies use public cloud platform. However, the popularity of cloud computing technology is on hype, and 2019 is poised to adopt new cloud technologies and help it achieve new heights.

Testing Challenges Scenarios with Real Estate Startups

The majority of the commercial real estate start-ups start utilizing some of the trendy technologies such as intellectual analytics, machine learning, Virtual Reality, Augmented Reality, etc. Besides, the frequency and impact of the project complexity factors has evoked the necessity to use software testing technologies that are able to facilitate the process of development, as well as coordinate the software solutions for real estate business. Consequently, Entrepreneurs from different industries have started using the power of advanced new technologies & testing tools to survive and thrive and to bring more efficiency to business processes. Fortunately RE start-ups have adopted plethora of software solutions, so we can observe numerous real estate startups thrives from rent management to virtual home tours.

Challenges of IoT implementation: Subsequent Challenges

a) IoT apps are ridden with manifold, real-time scenarios occurring in combo, which can be very painstakingly difficult or complicated.

b) Determining the scalability scale is at all times a knotty affair. It’s difficult because there are future upgrade concerns.

c) Testing scenarios are monitored and heavily controlled contrasting the real-time situations, which are vulnerable and volatile with millions of sensors and different devices working in synchrony. The fact the IoT apps, which may have scored a perfect score in testing, might fail to bring the best results in the actual ecosystem.

d) With IoT expansion, the security concerns over safety and data integrity persistently grow and are compel test engineers to keep their heads for corrective plans.

The present challenges of IoT implementation are overwhelming, attributable to the highly complicated and exceptional characteristics of IoT apps. This mandates diverse test scenarios for general use, day-long simulations, and peak points, to ascertain if these apps ensure total scalability and performance of the IoT architecture. Generally, IoT test scenarios are classified into 6 types:

1. Performance Testing: This includes real-time and far more cumbersome aspects, such as streaming analytics, load testing, timing analysis, and time-bound outputs to validate and guarantee consistent performance of data writing, data reading, and data retrieval.

2. Security Testing: Handle out an onslaught of data is fundamental to Internet of Things operations, and hence, companies must conduct security testing to remove vulnerabilities and manage the integrity of data. This concludes scrutinizing several aspects of the system, comprising data protection, device identity authentication, encryption/decryption, & more.

3. Functional Testing: It examines the quantitative and qualitative functional deliverability of deployed Internet of Things applications in the actual conditions. Aspects, like environment conditions, network size, and topologies, are put to test.

4. Compatibility Testing: Compatibility Testing assesses if the existing working combination of software, protocols, hardware, and OSs fall on the Internet of Things interoperability radar, and are compatible with the specifications and standards of conventional IoT industrial framework.

5. Scalability Testing: This comprises the testing of all functional as well as non-functional use cases to guarantee whether the system is easy to scale to accommodate future up gradation.

6. Regulatory Testing: Regulatory testing determines the compliance of Internet of Things applications with privacy regulations.

For this reason, it will be important for Real Estate startups to create a great testing strategy to face challenges that molds to their competence and application development requirements.

  • Use the MVP- When you are a startup business looking to make the most of your software testing opportunities, the minimum viable product is going to be the MVP you require. If in case project fails, it does not have to go to production, and major funds can be saved from using the MVP to appraise the overall concept. For startups business, getting the most value out of your hard work is imperative. Building a minimum viable product will cost less money and take less time to produce than create a monolithic program. Startups can’t afford to ignore this major testing strategy and should aim to use it as an essential point for their projects.
  • Invest in helpful resources- In addition to assess and utilize people that can take on testing tasks, these individuals should be provided with the best assets possible that will help them to be successful. It is better to first consider investing in agile test management. In an agile software development environment, collaboration and communication are highly praised, and test management can facilitate such practices. Even if your business starts out small, the test management techniques and tools easily scale along with your company, guarantee that you do not need to pay for added support. It will help teams make vital decisions and quickly patch up bugs in the build.
  • Utilize everyone to test- If you are a startup that has an elite and dedicated tester or QA team provider as a backup, you are miles ahead of the game. However, several startups and smaller organizations may not have the dedicated resources to devote toward Quality Assurance and testing. Software Testing provider like ImpactQA noted that everybody from developers to sales associates and business analysts can contribute to the testing effort. Testers can easily and assess the navigation and functionality of a program. Developers can assess and make changes straight to the code to fix any issues and promote a positive UI/ UX experience. “As a startup, you’ve got to prioritize,”

Automation is another crucial tool that you ought to consider. Not only can automation integration take off some recurring test cases from the workload, but it can also empower your QA team to expend extra time on GUI & exploratory testing. As a startup, you should consider choosing automation testing tools offered by QA Consultants wisely. Fortunately, QA Outsourcing companies in US like ImpactQA offers a wide variety of options, so you will be able to find one that fits your particular business needs.

“Taking the time to thoughtfully craft your testing strategy and the time to modify it as you progress will allow your startup to begin testing in the way that makes sense for you,” Thomson wrote.