New trends in agile test management

Digital transformation has changed the face of all of the industry. The software delivery model has undergone a drastic change with DevOps, agile and continuous delivery, leading these changes. Several businesses are successful in implementing agile processes to improve software delivery. But as the lines between development and operations are blurring, sprints are getting shorter, the difficulty mounts in meeting the higher expectations both for speed and quality of software deliverables.

An important and often missing link in the DevOps loop is testing. DevOps, in reality, is DevTestOps and for various teams and delivery models to be agile, test management is the vital link. Organisations need TestOps to match the pace of DevOps and by testing early and often, they gain the incremental quality benefits that bring true value to the business in terms of cost, efficiency, and continuous delivery.

In fact, the World Quality Report led by Capgemini shows that there is increased investment in the QA and Test function reported by 90% of US and 69% percent of Canadian survey participants in the past four years.

Challenges to DevTestOps
However, going agile and being agile are totally different stories and organisations face several practical challenges when trying to embrace the DevOps and agile way. Shorter sprints require better collaboration and integration, interoperability of tools. There are many gaps between conventional test management and modern Agile dev approach – with outdated tools and practices being a primary roadblock.

There are various other challenges within the software development lifecycle at every stage and communication gaps that slow its pace and weigh it down. Agile as it is practiced now, allows for delays in testing, leaving less time for testing and improvement leading to buggy releases and poor customer satisfaction. But with the Shift Left concept – the focus is on quality from day 1. Testers are part of the sprint right at the outset and prevention rather than detection is the modus operandi.

A Brand-New Approach
For Test Management to follow the Shift Left concept, it needs unified solutions, frequent test runs and more feedback. To accomplish the continuous integration and continuous delivery paradigm, continuous testing is necessary at all points within the development lifecycle and this requires a design thinking mindset and culture change. This means that developers, testers and ops teams need to reset the parameters of a traditional approach, more so when it comes to the testing processes.

The new trends in agile test management demand a fresh approach, a cultural shift and often new tools that speed up the execution.

Agile Test Management Trends Demand New Tools
Continuous integration and development and continuous testing, increased automation, behavior-driven-testing, predictive quality and prescriptive quality analytics – these are some enablers for agile test management. There is an increased focus on leveraging these disciplines and tools that help you implement them. Obviously, there are best practice recommendations for test management and how teams should be testing.

Best Practices Recommend
Focusing on collaboration and an integrated approach for test management amplifies the feedback loops and helps it to be truly agile. These are some of the other criteria:

Visibility
Traceability
Continuous integration
Behavior-driven development
Integrated toolset
Busting siloed workflows
Exploratory testing
Predictive analytics
When setting up your test management tool, it is advisable to assess your choices for a cloud-based solution. Ideally, one that allows you to set up roles and projects inside test management, provides greater visibility to your team, integrates your project management tools like JIRA with test management, lets you sync defects and test results in real time.

Configure your test management solution such that you can plug in your automation, generate API keys allowing your tool to accept automation results. Teams should be able to create or import requirements, with test coverage helping go/no-go decisions, modularity and linkability are again useful features to promote reuse and reduce authoring efforts. Exploratory testing and a comprehensive view of manual and automation testing – enable you to make better quality decisions. Also, creating test suites by release and platforms lets you track if the quality improves build by build.

The fresh approach to agile test management can get your delivery up to speed. It requires a fresh perspective and culture shift, and new tools that enable your agile development and testing efforts.

Source: softwaretestingnews

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U.S. consumers are spending 10 hours per year in shopping apps

Mobile shopping is on the rise, with U.S. consumers now spending nearly 50 minutes in shopping apps per month, or 10 hours per year, according to a new report out this morning from App Annie. Digital-first shopping apps, like those from Amazon, Etsy, Wish and others, are also growing more quickly in terms of total sessions and monthly usage, compared to shopping apps from traditional brick-and-mortar retailers, the report also found.

In the U.S., the top 5 digital-first shopping apps saw more than 60 percent growth in total sessions year-over-year, during the first half of 2017, compared with just 50 percent growth in traditional retailers’ digital apps.

In terms of average monthly sessions in the U.S., digital-first apps grew nearly 25 percent, compared with 15 percent for those from brick-and-mortar retailers.

This is one area where Amazon is beating Walmart, it seems. Digital-first apps like those from Amazon also saw 19 sessions per month, compared to only 12 sessions per month from brick-and-mortar retailers, like Target and Walmart, during the first part of the year, says App Annie.

The top five digital-first apps by time spent in the U.S. during H1 2017 were Amazon, Amazon Shopping (Amazon had an old app that was removed from the app store, but still ranked in this chart), Wish, Etsy and Zulily. Amazon Shopping was also the top app in the U.K. and Germany, and the number two app in Japan.

Amazon, followed by Wish, Etsy, AliExpress, and Amazon Prime Now were also the top apps in the U.S. during H1 2017 by monthly active users.

Meanwhile, the top five apps from traditional retailers by time spent in the U.S. during H1 2017 were Walmart, Cartwheel (Target’s app, which is now in the process of merging with Target’s main app), Kohl’s, The Home Depot and Kroger.

By monthly actives, the U.S. list included Walmart, Walgreens, Cartwheel, Kohl’s and Target.

Walgreens, in particular, has seen surging growth in users, up 65 percent year-over-year in H1, noted the report.

In addition, people tend to use the apps from brick-and-mortar retailers while on the go – perhaps while browsing the aisles, or trying to locate items in the store. Meanwhile, the digital-first apps were more often used while on Wi-Fi – indicating the shopper was likely either at work or home when they launched the app.

These findings will come into play this holiday shopping season when App Annie predicts that users will spend more than 12 million hours in the top 5 digital-first Android apps in the U.S., on Black Friday and Cyber Monday combined, or 40 percent more time than last year.

The firm also predicts an increase in app-only deals and exclusives, and exclusive deals at brick-and-mortars with new in-store features, like Target’s visual search or Walmart’s Scan-and-Go.

Source: Techcrunch

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FinTech Accelerator Findings - A Study Revealed By Bank Of England

The Bank of England published the results of an accelerator focused on FinTech.

On October 6, 2017, Andrew Hauser, executive director for banking payments and financial resilience for the Bank of England (BoE), remarked on a FinTech accelerator that the central bank launched in June 2016.
According to Hauser, the accelerator was endeavored upon with two main objectives. The bank sought primarily to improve general familiarity with products in the FinTech ecosystem in order to better assess their strengths, weaknesses, and marketplace applications. The secondary objective was to provide insight to firms regarding possible regulatory, policy, and operational implications of technologies that are rising in popularity, such as distributed ledger technology (DLT).
Hauser affirmed how the merits of DLT can be applied to banking systems.
"DLT has in many respects been the poster-child for FinTech. Thrown into prominence by the advent of bitcoin and other cryptocurrencies, the attraction of DLT to central banks really lies in the potentially highly attractive resilience characteristics of the underlying technology," explained Hauser. "In its purest form, a DLT network operates with no centre, and every node in the network holds a full copy of the ledger. So the failure of a node has no impact on the overall resilience of the system, with transactions simply rerouting elsewhere."
Hauser went on to say that valuable connections made by the BoE in the FinTech sector are a result of the accelerator and BoE's broader field work.
He described how research has impacted BoE's understanding.
"Our work on DLT has helped us start to think through how the financial networks of the future may be able to operate in safer and more efficient ways … Our work on data analysis has thrown light on how we can manage ever larger data sets to monitor the economy and the financial system in real time and draw out patterns that might help us set better policy or spot the next crisis coming before it happens. And our work on machine learning has helped us take the first baby steps towards engaging with that data in a more interactive way, putting computers alongside our staff to help them form the judgments on which monetary and financial stability depend."
In May of 2017, the BoE said DLT lacked the maturity necessary to manage payments. However, BoE also recently celebrated 20 years of independence in monetary policy, during which, International Monetary Fund (IMF) Managing Director Christine Lagarde extolled the virtues of new technology, even hinting that Special Drawing Rights, the in-house IMF money that is backed by an average of various other currencies, might one day encompass cryptocurrencies. The recent findings of the accelerator, coupled with IMF sentiment, might be the preface of a pivotal stance by the BoE regarding the application of DLT or other technologies, such as blockchain.
Source: ETHnews

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11 Internet of Things Stats That Will Blow You Away

The Internet of Things (or IoT) sounds like one of those futuristic buzzwords that's still just a little too far off to think much about. But the IoT -- where once-unconnected things like watches, cars, healthcare equipment, etc. will be connected to the Internet -- is already here, and it's changing our health, how we build things, and how we get around, and creating billions of dollars in value across multiple sectors.

So let's take a look at some of the most mind-blowing IoT stats and why all of it matters for investors.

1. Research firm Gartner says that IoT devices have increased 31% from 2016 to 2017, hitting 8.4 billion connected "things" this year, and that the number will surge to 20.4 billion by 2020.

2. To help put the amount of IoT devices into context, consider that Ericsson predicts that the amount of IoT devices will surpass mobile devices by next year.

3. Spending on IoT devices and services will reach nearly $2 trillion this year. That spending will mostly be spread across North America, China, and Western Europe, where  about 67% of IoT devices exist.

4. Consumers are still driving IoT device growth right now, and will account for 5.2 billion IoT devices this year, which represents 63% of the market.

5. Wearable devices like Apple's (NASDAQ:AAPL) Apple Watch and Fitbit's (NYSE:FIT) fitness trackers are growing in popularity, and wearable until sales will reach 82.5 million in 2020, according to IDC. The competition is already heating up in this segment, with China-based Xiaomi's wearable devices leapfrogging shipments of Apple's wearables and Fitibit's devices for the first time last quarter.

6. Consumers may be the early adopters for IoT devices, but business are spending more on the IoT market overall. Companies will spend $964 billion on IoT hardware this year, compared to consumer spending of $725 billion. And in just three years the combined consumer and business markets will spend $3 trillion on IoT hardware.

7. According to a PTC report, manufacturing will be the biggest IoT platform by 2021, reaching $438 million as the Industrial Internet of Things (or IIoT) increases efficiency and decreases downtime for manufacturing companies. A separate study by Accenture says the IIoT could help reduce machinery breakdowns by 70% and reduce overall maintenance costs by 30%.

 8. The surge from industrial companies using IoT devices should have very positive results for economies around the world. Accenture estimates the the IIoT will add $14.2 trillion to the global economy by 2030. That's great news for IoT pure plays like CalAmp (NASDAQ:CAMP), which sells hardware, software, and services that connect industrial equipment to the Internet. CalAmp made about 86% of its total 2016 revenue from its mobile resource management (MRM) technologies and machine-to-machine (M2M) communications tech.

9. IDC expects 80% of consumer service interactions in the healthcare industry (like meeting with your doctor) to use IoT and other analytics services by 2020. Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google recently relaunched its Google Glass device and found that that using it allowed doctors to easily access patient information and decrease the amount of time it took them to take notes -- both of which resulted in more time spent with patients.

10. The Internet of Things also has the ability to improve our quality of life beyond the doctor's office. Bosch estimates that Internet-connected cars will reduce traffic accident injuries by 350,000 every year by 2025, and save 11,000 lives each year.

11. According to BI Intelligence research, agricultural IoT device shipments will jump from 43 million this year to 75 million in 2020. Agricultural companies are investing in technology that helps them to know where, when, and how much seed to plant using sensors, pre-planned seeding maps, and GPS-connected equipment. This precision farming and smart agriculture will make growing food more efficient and help farmers keep up with rising food production needs.

What investors should remember

The IoT will transform many different industries, but investors should remember that not all companies are betting on the IoT at the same scale. For example, the Apple Watch leads the smartwatch pack, but Apple brings in just 6% of its total revenue from sales of its "other products" (which include the Watch, Apple TV, Beats headphones, and other accessories). Apple could eventually become a bigger player in the wearable devices space if it launches its rumored augmented reality glasses, but at this point the iPhone maker doesn't have tons of IoT exposure. A better bet may be CalAmp, with its its industrial IoT equipment and services, which makes nearly all of its revenue from the IoT.

Additionally, the IoT faces some serious security risks. The Mirai botnet attack in 2016 targeted IoT devices and used them to make a Distributed Denial of Service (DDOS) attack (when a server is flooded with so much traffic that it crashes). That resulted in Netflix, Shopify, Twitter, and other sites going offline for a while. The cost of adding connectivity to devices and other things continues to come down, which is great for IoT device expansion -- but it has also made it easy for smaller players to release devices that aren't secure.

All of this means that IoT investors should look for solid businesses in established markets, and bet on them over the long-term. The Internet of Things is growing quickly, but it will still take years for it it to mature and for some companies to see the benefits.

Source: Fool

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What you should know about AI

Artificial intelligence seems to be nearly everywhere these days, yet most people have little understanding of AI technology, its capabilities and its limitations.

Despite evocative names like “artificial intelligence,” “machine learning” and “neural networks,” such technologies have little to do with human thought or intelligence. Rather, they are alternative ways of programming computers, using vast amounts of data to train computers to perform a task. The power of these methods is that they are increasingly proving useful for tasks that have been challenging for conventional software development approaches.

The commercial use of AI had a bit of a false start nearly a quarter century ago, when a system developed by IBM called Deep Blue beat chess grand master Garry Kasparov. That generation of AI technology did not prove general enough to solve many real-world problems, and thus did not lead to major changes in how computer systems are programmed.

Since then, there have been substantial technical advances in AI, particularly in the area known as machine learning, which brought AI out of the research lab and into commercial products and services. Vast increases in computing power and the massive amounts of data that are being gathered today compared to 25 years ago also have been vital to the practical applicability of AI technologies.

Today, AI technology has made its way into a host of products, from search engines like Google, to voice assistants like Amazon Alexa, to facial recognition in smartphones and social media, to a range of “smart” consumer devices and home appliances. AI also is increasingly part of automobile safety systems, with fully autonomous cars and trucks on the horizon.

Because of recent improvements in machine learning and neural networks, computing systems can now be trained to solve challenging tasks, usually based on data from humans performing the task. This training generally involves not only large amounts of data but also people with substantial expertise in software development and machine learning. While neural networks were first developed in the 1950s, they have only been of practical utility for the past few years.

But how does machine learning work? Neural networks are motivated by neurons in humans and other animals, but do not function like biological neurons. Rather, neural networks are collections of connected, simple calculators, taking only loose inspiration from true neurons and the connections between them.

The biggest recent progress in machine learning has been in so-called deep learning, where a neural network is arranged into multiple “layers” between an input, such as the pixels in a digital image, and an output, such as the identification of a person’s face in that image. Such a network is trained by exposing it to large numbers of inputs (e.g. images in the case of face recognition) and corresponding outputs (e.g. identification of people in those images).

AI will not replace software, as electricity did not replace steam.

To understand the potential societal and economic impacts of AI, it is instructive to look back at the industrial revolution. Steam power drove industrialization for most of the nineteenth century, until the advent of electric power in the twentieth century, leading to tremendous advances in industrialization. Similarly, we are now entering an age where AI technology will be a major new force in the digital revolution.

AI will not replace software, as electricity did not replace steam. Steam turbines still generate most electricity today, and conventional software is an integral part of AI systems. However, AI will make it easier to solve more complex tasks, which have proven challenging to address solely with conventional software techniques.

While both conventional software development and AI methods require a precise definition of the task to be solved, conventional software development requires that the solution be explicitly expressed in computer code by software developers. In contrast, solutions with AI technology can be found automatically, or semi-automatically, greatly expanding the range and difficulty of tasks that can be addressed.

Despite the massive potential of AI systems, they are still far from solving many kinds of tasks that people are good at, like tasks involving hand-eye coordination or manual dexterity; most skilled trades, crafts and artisanship remain well beyond the capabilities of AI systems. The same is true for tasks that are not well-defined, and that require creativity, innovation, inventiveness, compassion or empathy. However, repetitive tasks involving mental labor stand to be automated, much as repetitive tasks involving manual labor have been for generations.

The relationship between new technologies and jobs is complex, with new technologies enabling better-quality products and services at more affordable prices, but also increasing efficiency, which can lead to reduction in jobs. New technologies are arguably good for society overall because they can broadly raise living standards; however, when they lead to job loss, they can threaten not only individual livelihood but also sense of identity.

An interesting example is the introduction of ATMs in the 1970s, which transformed banking from an industry with highly limited customer access to one that operated 24/7. At the same time, levels of teller employment in the U.S. remained stable for decades. The effects on employment of automation because of AI are likely to be particularly complex, because AI holds the potential of automating roles that are themselves more complex than with previous technologies.

We are in the early days of a major technology revolution and have yet to see the great possibilities of AI, as well as the need to address possible disruptive effects on employment and sense of identity for workers in certain jobs.

Source: Techcrunch

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Get with the times: Insurers embrace big data, fintech to cut cost, improve services

The Insurance Authority is encouraging local providers to embrace fintech while Allianz, Zurich Insurance, FTLife and FWD have invested heavily in new technology

Hong Kong’s newly set up insurance regulator is encouraging insurance companies to develop financial technology, as leading providers embrace the big data revolution to manage risk and lower the cost for their products.

‘We are very keen on promoting fintech in the insurance industry. We are not taking a leading role but we plan to help the industry to use technology to enhance their services and better manage risks,” said Moses Cheng Mo-chi, chairman of the Insurance Authority.

German insurer Allianz is among industry players which are keen on fintech development and using big data to better design products and handle claims.

“The use of big data will benefit not just the insurance companies but also customers as it will drive prices down up to 30 per cent over the following years,” said George Sartorel, regional chief executive of Asia-Pacific for Allianz.

Sartorel said traditionally insurance companies priced their policies on a system that relied on statistical averages which could not reflect an individual’s behaviour.

The usage of big data, which Allianz has adopted in recent years, has changed that.

As an example, Allianz invited drivers to take part in a voluntary monitoring system using GPS tracking for speed, driving routes, frequency and other driving behaviour.

In theory, the data can help determine future policy pricing. Those who drive at high speeds or who frequent accident-prone routes are likely to face higher insurance premiums, Sartorel said.

“There was an old joke that the driver might tell the insurance company that it was his grandmother who used the car. However, with the new technology of big data, we can spot the different behaviour of different drivers who use the same car,” he said.

“The beauty of the usage of big data is that it can allow insurance companies to provide different pricing models to meet with different needs of different customers. Some customers who only drive on weekends could buy motor insurance priced on per kilometre usage,” he said.

He said if drivers know their behaviour is being tracked remotely by insurance companies, they tend to become more carefully and hence reduce accidents on the road.

Zurich Insurance, one of largest general insurers in Hong Kong, is also keen on fintech.

“Our digital app has been available in the market for years for customers to submit claims for car accidents, medical expenses, travel plan interruptions and other circumstances,” said Eric Hui Kam-kwai, chief executive officer of Zurich Insurance (Hong Kong).

“Great companies are keen to innovate. It is important that we re-engineer the customer experience and strengthen internal operations through the initiatives we are now putting into place. Our new online claims portal, eClaim, an expansion of our claims touchpoints, is a result of constantly asking how we could improve for our customers,” Hui said.

FWD Hong Kong, another major Hong Kong life insurer, will invest HK$500 million in the development of proprietary InsurTech solutions in the next five years, more than five times the investment in this area over the past three years, a company spokesman said. The company will invest in three core areas, namely mobile services, internet of things (IoT) and big data analytics.

Life insurer FTLife, which was bought by mainland investment firm JD Group in 2015, also plans to expand further in technology in the next few years, according to regional chief executive Lennard Yong.

Likewise, Allianz also applies big data on some health products such that people who have more heathy life styles would enjoy lower premiums.

Raymond Au, regional head of data science of Allianz, said Asia is moving faster in insurance technology than Europe.

“Asia particularly China is developing big data very quickly,” Au said.

The changing data landscape has led to technology firms such as Tencent and Alibaba, which owns the Post, to enter the insurance sector.

“Traditional insurance companies are facing challenges from technology firms which have expanded into the insurance sector with new technology. This is why Allianz has placed high importance on digitalising our business model across Asia. We have to change ourselves very quickly to meet with the technology changes,” Sartorel said.

Source: SCMP

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Infrastructure as code: The agile approach to testing

The purpose of testing is to help us to get our work done quickly, however, in many organisations, testing is seen as something that slows work down.

There is a common misconception that quality and delivery speed are opposing forces that must be traded off against each other, with this mind-set leading to the idea that automation can speed up the delivery process by making the act of testing a system go faster. These misconceptions can easily lead to expensive, failed test automation initiatives.

Quality is an enabler of delivery speed, with the goal of automated testing being to help teams focus on keeping the quality of their system high, through fast feedback. When combined with a good team culture and a discipline that prioritises quality, automated tooling can help to find quality issues fast, meaning the team can respond and fix them quickly.

In turn, this keeps the system in a state where changes can be made quickly, easily and confidently, proving that faster delivery speed is a side effect of focusing on quality and automated test tooling is an aid to keeping quality at the forefront of the team’s mind.
Shortening the feedback loop

Agile processes encourage teams to integrate testing with implementation, in order to shorten the feedback loop. Testing takes place continuously, with ongoing changes being made by testers and developers working closely together, combined with automated testing.

The most useful goal for test automation isn’t to make a test phase run faster, but to enable testing and fixing activities as a core part of the workflow. As someone works on changes to the system, whether that is to an application code or infrastructure definitions, they are continuously testing. People test so they can fix each problem as it is discovered, while they’re still working on their changes and everything is fresh in their mind. When the scope of changes are very small, the problems are quick to find and easy to fix.
Automating tests for fast feedback

Teams whose testing process is based around separate implementation and test phases often attempt to adopt automation by automating their test phase. This is often a project owned by the QA team, which aims to create a comprehensive regression test suite. In my experience, automated test suites built by a separate testing team tend to focus on high level testing, but the outcome can sometimes result in an unbalanced test suite.

The key to designing and implementing a well-balanced automated test suite is for the entire team, especially the implementers, to be involved in its planning, design and implementation. Big bang test automation initiatives often bite off more than they can chew, and struggle to keep up with ongoing development. The system is a constantly moving and changing target, and before the massive test suite is complete, the system has changed and shifted multiple times. Assuming the test suite can be completed, the system will change again immediately, meaning tests tend to be constantly broken, and the nirvana of a complete test suite is never achieved.

It is rarely effective to aim for the goal of a complete, finished test suite; the goal of an automation initiative should be to embed the habit of continuously writing tests as part of routine changes and implementation work. The outcome of an automated testing initiative is not a completed test suite, but a set of working habits and routines. When automated testing has been successfully adopted by a team, tests are written or updated whenever a change is made to the system. CI and CD regimes run the relevant tests for every change continuously, and the team responds immediately by fixing failing tests.
Organically building a test suite

The best way to start an initiative that results in embedding these kinds of testing habits is to write tests for each new change as it comes up. When a bug is found, write a test that exposes that bug, and then fix it. When a new feature or capability is needed, begin implementing tests as you go, possibly even using TDD. Building the test suite organically as a part of making routine changes forces everyone to learn the habits and skills of sustainable, continuous testing.

The outcome to aim for is not a “finished” test suite, but the routine of testing each change, and a comprehensive test suite will emerge from this approach. Interestingly, the test suite that emerges will be focused on the areas of the system that need tests more urgently and the ones which change and/or break the most.
Implementing automated infrastructure testing

There is a variety of tooling available to implement automated infrastructure testing, and in many cases, tools designed for software testing can be directly adopted and applied to infrastructure. Some of these tools have been extended to add infrastructure specific functionality; Serverspec, for example, extends the RSpec Ruby-based testing tool with features for checking server configuration. It’s important to avoid getting hung up on the tooling, however, and you should avoid choosing a tool and basing your entire testing strategy around it.

Instead, analyse the systems and components at hand to decide how you need to approach testing them, and then find tools to carry out your approach. As with any part of your infrastructure, you should assume that you will continuously change parts of your test tooling over time.
Roles and workflow for testing

Infrastructure teams tend to find testing a challenge, with the typical systems administrator’s QA process being: 1) make a change, 2) do some ad-hoc testing (if there’s time), 3) keep an eye on it for a little while afterwards. On the flip side, some testers don’t understand infrastructure very well, and as a result, most testing in IT operations tends to be at a fairly high level. One of the big wins of agile software development is the breaking down of silos between developers and testers, and rather than making quality the responsibility of a separate team, developers and testers share ownership. Similarly, rather than allocating a large block of time to test the system when it’s almost done, agile teams begin testing when they start coding.

There is still some disagreement over what the role of a QA (Quality Analyst) or tester should entail, even within an agile team, with some teams deciding that, since developers write their own automated tests, there is no need for a separate role. Personally, I find that even within a highly functioning team, QAs bring a valuable perspective and level of expertise for discovering the gaps and holes in what I build.
Conclusion

Automated testing is arguably the most challenging aspect of infrastructure as code, whilst also being the most important for supporting a reliable and adaptable infrastructure.

Teams should build the aforementioned habits and processes to routinely incorporate testing as a core part of their infrastructure, but should recognise that this will require the highest degree of openness to change.

Source: http://www.itproportal.com/2016/04/24/infrastructure-as-code-the-agile-approach-to-testing/

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What to look out for when approaching software testing?

With increase in the evolvement of software development process maturity, software testing processes need to adapt to quality attribute needs and customer requirements. Software companies have been pressurized to deliver quality software products more quickly. It is creating a competition between software companies to gain market leadership. To achieve success in this competition, these companies need to run development activities along with testing very efficiently. A thing that one should look out for when approaching software testing is the value or ROI in terms of reduced testing time, reduced efforts, reduced costs from the amount that is being invested.

With constant increase in the application complexity, software companies cannot avoid testing whether it has to be implemented at the end of the cycle or to be performed throughout the development life cycle. Many companies face sudden increase in testing costs due to not having a proper test strategy and efficient operational capability. The problem also might rise when any defect goes missing and finding it after the launch of the product or even when any defect goes into the next stages of life cycle.

The power of Internet has been helping companies all across the world in reaching out to the increasing online customers through promoting their products. To attract more and more online customers, these online business based companies want their websites or web applications to perform flawlessly at all times. They are looking out for quality application to support the growth of their business. To identify quality of their application, they are approaching software testing to identify the Functional, Performance, Security, and compatibility issues before customer does.

People who think that software testing is a tedious, time-consuming and it consumes a lot of money, people look out for software testing services that come for very low cost and the ones which is in their budget. Many of the people are not aware of the reality that an application needs to undergo comprehensive or End-to-End Testing to be able to perform flawlessly in the real time. Additionally, due to the time and budget constraints, some people only perform Ad-hoc testing. Ad-hoc testing is what some stake-holders likes to do it post it goes through comprehensive testing, but it doesn’t help in identifying all the latent issues in an application when it is carried in the actual testing time. It could bring heavy loss and harm reputation for the businesses.

It is crucial to find an appropriate software testing vendor who can adapt to changing requirements. If the company is performing testing using its in-house test environment, the company needs to find appropriate tool, appropriate resource for the right task. On the other side, there is an increasing demand for Automation Testing which is helping to test faster. This still not help an application as it has to get tested with many other types of testing. It suggests that one should have multiple testing tools, relevant test engineers and required infrastructure. All these are challenges which lead to time consuming.

For example, people who perform only functional testing of their applications, there can be an issue with the performance of an application, because performance testing is also important. Moreover, there are other types of testing, which need to be deployed to cover all the quality attributes. It might be due to time and budget constraints that companies skip End-to-End testing. However, cloud-based software testing providers are emerging in a rapid pace and most of them are providing testing solution or service on a ‘pay for what you use model’. Cloud based testing could help businesses of any size in saving a lot of cost and time. Not many companies are offering End-to-End software testing on cloud. Companies need to approach a vendor who can provide reliable and effective End-to-End Automation testing. It would be right solution to reap maximum value out of testing.

Source: https://www.clictest.com/

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Software Testing Myths and Realities

Many companies see software testing at in a different way that depend on web applications for business operations in today’s rapidly transforming technology. These companies think software testing as a mysterious thing which has factors that are difficult to understand. Things like why and when they need to have pool of expert test engineers, next-generation testing tools with modern infrastructure and a key thing is that difficulty in finding the priorities of testing. The most common myths are that testing is time-consuming, it is too costly, it requires a lot of effort, testers are responsible for the inadequate quality of application, testing in agile environment is purely ad-hoc etc.

Companies that looking out for testing their applications or the companies that looking out to outsourcing testing activities would face a lot of factors which keep hitting their minds. Companies that are not aware of capability of software testing would be completely puzzled. It is not easy to have all the right things in place at the right time.

Some people think it is too boring at times when the need of being creative in work is very limited. It could happen when the software project is very small with only few functions and user input fields here and there. Of course it will not last long, but it’s not very exciting either. However, if a project is a large one with a lot functions and features, it would be very interesting and challenging to test. It needs applying a lot of creativity to be productive.

Some software testing stories that could really confuse one with certain misconceptions like some; people think that testing is too costly, but for the reality it is something like you pay less for testing during software development or pay more for identifying issues later. Introducing software testing at the early stages would help in reducing both time and cost. Some companies think that testing is time-consuming, but for the reality when it is introduced during the development life cycle, it is never a time-consuming activity. Testing and identifying bugs throughout SDLC is always very productive.
Some companies think that tester’s only job is to find bugs, if bugs are not identified, they would be responsible for quality of application. But the reality is that, there cannot be anything like that an application is defect-free even through when it is tested through the specialist team. With an advent of Automation Testing, people started thinking that it can be used anywhere during SDLC. But the reality is that, it can help only in reducing the repetitive tests.

Another thought is that automation can eliminate the need for manual testing. Doubtlessly, there is no replacement for manual testing, whereas automation testing is relied on manual test plans only. Generally, automation is deployed only when tests are repetitive and time-consuming. A better test coverage can be reaped using the combination of manual and automated testing. Some myths around Testing in Agile environment like, since agile development methodologies have been focused point for many companies, testing in agile environments has become important.

Some myths are; Testing in Agile is ad hoc, less documentation, and it does not have strategies. For the reality, an agile environment involves planning sprints, budget and resources ahead of time. It brings testers and developers together helps to improve quality, achieve faster time to market at reduced costs. However, the testing has moved on. Testing cannot be ignored. We should be focusing at increasing complexity of applications and how can we test them to identify all defects.

Source: https://www.clictest.com/

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The Perks Of Cloud based Test Automation Tools

The future belongs to everything ‘Cloud’. Take the example of the Hybrid Cloud Model, which is gaining traction in many enterprises. The same can be said for cloud-based software testing tools, which are making their presence felt on the development scene.
Let us find out more about what advantages mobile app developers can avail by using such tools.

Multiple uses
Cloud-based versions of the tools can be used for functional testing, performance testing, and many other testing types. In short, they can be used as a complete test management tools. This means that for all these kinds of testing, you don’t need to procure different kinds of testing tools, but rather one that fits all of your requirements.

Start using almost instantly
Cloud-based test automation tools are ready for use the very moment you buy them. No more installation woes, setup requirements, hunting for servers, or prepping of hardware to start using them. This means that it reduces a lot of effort required from the IT management teams and puts the focus back on the core functionalities of an enterprise.


A user-friendly interface
More often than not, cloud-based automation tools have an incredibly user-friendly interface. This makes them quite easy to use, even for novice developers, as there is hardly any special training required for the software.

Speeds up the testing process
While automation tools are known for increasing productivity in general and shortening test cycles, the cloud-based version of these tools brings forth even more advantages. They not only speed up the entire testing process by being independent of browsers and devices, and getting rid of installation hassles, but they also come with seamless upgrades (leading to a reduced downtime).
All of these features allow you complete the testing process within the stipulated time frame, or possibly even before that. The additional time on hand can be spent on targeting other steps of the app development process, for instance various marketing and promotional activities.

Competitive price range
When you compare them to the regular test automation tools, you will find that the cloud-based ones are available at a competitive price. This is obvious from the fact that you need not spend a considerable amount of money to upgrade the hardware of your device(s).
There is practically zero expenditure for expensive licenses and most importantly, you save up by not having to pay for a manual testing team. This works for most companies, especially the ones who are looking to cut down on their expenses.
Moreover, the option of ‘pay as you use’ lets you use the tools only when it is necessary, and therefore, saves on the costs later when you are not using them.

Ease of collaboration
Since cloud-based automation tools provide the option to work anywhere and using any device, it makes it easy for teams in different locations to go through test reports, make modifications, or ask queries without any hassle. This significantly reduces the steps in completing testing, saving yet another valuable resource for companies – time.
This gives numerous companies, especially startups, a competitive edge. For instance, if they have a globally dispersed team located at the opposite ends of the world, they can still collaborate on the most complex projects using cloud-based tools to test their applications.
Think of it this way – there is going to be less paperwork (albeit virtually) when you don’t need to constantly send or receive and go through endless test reports regarding your project. All in all, this speeds up decision-making, and hence helps in speedy delivery of the project.

Greater control to development teams
There is no doubt about the fact that cloud-based testing systems give you more control to schedule and execute tests in the best possible way. You can use them to conduct a thorough analysis of applications and check them for possible bottlenecks. This is good news for development teams, who appreciate tools that offer them greater control over their projects and allow them to handle them in the way they want.

Immense flexibility
One of the benefits of cloud-based test automation tools is that they can operate without depending on a specific browser or an operating system. This gives you the freedom to test your software anywhere and everywhere, using just about any device.
Through using these tools, you can easily run all your test cases and report them in the cloud, which can be an enterprise’s private cloud or a hosted one. Such flexibility is rarely offered by any other test automation tool on the market.

Scalability
Another important benefit is that the use of such tools never depends on the scale of the project, or even the complexity of the project for that matter. This makes it easy for you to test complex mobile apps as well as the simple ones. There is no end to the testing scenarios that you can explore, owing to the scalability offered by these tools.

Enhanced security
One of the biggest concerns that hover around all topics ‘cloud’ is security! It is true that cloud computing does have security issues, especially when it comes to larger enterprises, however most of them are nothing but general misconceptions. You can be assured that security concerns are being addressed by the higher-ups in the technology industry, which makes these tools secure for usage.

Conclusion
Although the combination of cloud with tried-and-tested approaches does work in the favor of enterprises and developers, it does come with a red flag. While it can cut down on costs, resources, and time expenditures, improper use of such testing environments can do just the opposite. The solution is to analyze your requirements, and complete a thorough round of research about the tools on the market before making a decision.

Source: http://blog.utest.com/2015/09/24/the-perks-of-cloud-based-test-automation-tools/

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