Category Archives: Automation

For some time, we said that data is the new oil to indicate how much value and power there is in having data available. Having data means having knowledge of whats happening and being able to objectively evaluate phenomena that otherwise could only be guessed. Then we started to say that having the data wasnt enoughits more important to apply context to data so that it can be transformed into information. Data is important, but providing context makes it much more meaningful, and the information can then be used to make better informed decisions.

This is even more significant when we are talking about the high volumes of data collected from manufacturing operations. These huge amounts of dataprocess and production datacan be difficult to interpret if you look at it without context. You can, for example, collect the temperature of an oven every second in several areas of the equipment, and have a very detailed dynamic view of whats happening in it. But it doesnt mean a whole lot if you dont know which SKU was being produced, what the setpoint was, and maybe what the humidity was of the semi-finished good that you were cooking or drying. So context transforms data in information; context is the refinery of data.

But today, even information is not enough. Transforming Big Data into Big Information is powerful, but it can still be difficult to interpret and understand. Moreover, when you apply context to data, you are basically applying a model that combines variables you know are correlated in some way. But is that the only existing correlation? Or are some variables correlated to others in way you do not know and maybe are not so evident? The exponential growth of available data and information makes it difficult, if not impossible, to evaluate all the possible relationships, especially when you start to consider data coming from different domains (e.g. process and business data) or data coming from different stages of the value chain.

This is when analytics becomes important and can transform information in the same way context transforms data. Analytics is a generic word to identify a set of different activities or applications of statistical analysis or business intelligence, sometimes related to a specific domain, others to a specific type of content. Frequently, it indicates the capability to apply statistical models or mathematical algorithms to a data set, distilling information that otherwise couldnt be retrieved and that can be used to predict possible situations or to support manual decisions or even to implement automatic decision processes.

In manufacturing, analytics often refers to a system that can analyze a set of data and automatically identify relationships between variables. In this way, the system builds a mathematical model that can be used to predict the state or value of a single variable based on the behavior of the others. One of the most used examples is predictive maintenance where, based on the data collected from several sensors installed on an asset, the system can predict if the asset will fail in the near futureoptimizing the maintenance process, and minimizing the maintenance costs and possible impact of a failure on production at the same time.

But this is just a very simple case to understand. Even within manufacturing operations that are considered best in class, the use of advanced analytics could reveal further opportunities to increase yield. This was the case at one established European maker of functional and specialty chemicals. It boasted a strong history of process improvements since the 1960s, and its average yield was consistently higher than industry benchmarks, so they were skeptical that there was much room for improvement. However, several unexpected insights emerged when the company used neural-network techniques (a form of advanced analytics available in many products) to measure and compare the relative impact of different production inputs on yield. By adjusting the process parameters based on the evidence, the chemical company was able to reduce its waste of raw materials by 20 percent and its energy costs by about 15 percent, thereby improving overall yield.

More and more opportunities become available by mixing data coming from different contexts. In this case, not only can analytics apply a mathematical model to very large volumes of data, but it can identify patterns and correlations that otherwise would be extremely difficult to identify, since nobody has full knowledge of the data set.

Analytics can really change the way a company is run, providing insights with a much larger value than information and data. Analytics is the third level of knowledge that promises to transform dramatically how people will manage factories, both at the operations level and at the business level.

Luigi De Bernardini is president of Autoware Digital and CEO of Autoware, a certified Control System Integrators Association (CSIA) member based in Vicenza, Italy. For more information about Autoware, visit the Autoware profile on the Industrial Automation Exchange.

See the rest here:

AnalyticsBeyond the Value of Information - Automation World

There's no shortage of studies and analysis suggesting that robots can potentially take our jobs. But exactly how far away are we from losing our livelihoods to automation?

Artificial intelligence experts with the BBC surveyed 352 scientists about automation, including some of the world's leading experts on machine learning. According to the BBC analysis, there is a 50% chance that machines can take over all human jobs in 120 years.

But some fields are at greater risk than others. Let's focus on one big one: trucking.

Truck drivers may be replaced by automated technology as early as 2027. According to the researchers, artificial intelligence could be maneuvering trucks on the road within the next decade.

"All jobs are being impacted by technological change some more than others," said Nicholas Wyman, CEO of the Institute for Workplace Skills and Development and author of Job U: How to Find Wealth and Success by Developing the Skills Companies Actually Need. "Driverless trucks are now used extensively in the mining industry and it's certain this technology will impact other parts of transport and distribution."

Estimates from the American Trucking Association suggest there are 3.5 million professional truck drivers in the United States and the industry, as a whole, employs more than 8.7 million people. According to the Los Angeles Times, 1.7 million American truckers could be replaced by self-driving trucks over the next decade.

Trucking jobs are the most common jobs in 29 out of 50 states in the U.S., and there are millions of people working for the trucking industry in non-driving positions.

Uber-owned Otto is perfecting the technology that will let trucks drive themselves.

It's not such a far-fetched idea, as progress is being made in automating truck driving. Take self-driving truck company Otto, which was created by former Google employees and acquired by Uber last August. The company's system lets trucks drive for long stretches of time without needing a human driver. The technology was successfully tested in Colorado in October when a self-driving truck delivered 50,000 cans of Budweiser.

If perfected, autonomous trucks could offer heightened efficiency and safer roads; one in seven fatal truck accidents is caused by driver fatigue. For those currently in the trucking industry, it's not all bad news. How much of a threat self-driving trucks pose to drivers depend on the level of automation: if drivers are still required to be in trucks, then jobs are secure, MIT Technology Review reports.

Wyman says truck drivers need to be open to adapting to the changing landscape of their field. "They need to embrace change it's happening so hoping it will go away is not an option," Wyman said. "Truck drivers should look for opportunities to refresh and reboot their current skill sets."

Original post:

When will automation take over the trucking industry? Scientists now have an estimate - Mic

Automation in the Information Age is different.

Books we used for this video:

The Rise of the Robots: http://amzn.to/2sFQTed

The Second Machine Age: http://amzn.to/2szATee

Support us on Patreon so we can make more videos (and get cool stuff in return): https://www.patreon.com/Kurzgesagt?ty=h

Robot Poster & Kurzgesagt merch here: http://bit.ly/1P1hQIH

The music of the video here:

Soundcloud: http://bit.ly/2sfwlJf Bandcamp: http://bit.ly/2r17DNc Facebook: http://bit.ly/2qW6bY4

Study about job automation in the next two decades: http://bit.ly/1mj2qSJ

THANKS A LOT TO OUR LOVELY PATRONS FOR SUPPORTING US:

Brandon Eversole, Andrew Anglehart, Christian Ahlin, Kathleen Woolum, Estel Anahmias, Adam Schlender, Mike Luque, Encyclo, Stevie Taylor, Brent Yoder, Invisibleman, Jeff Lam, Christopher Hayes, Oliver Walker, gwendolyn bellermann, Matt Logan, Philip Chou, Brandon Young, Arlo Stewart, Thomas Hodnemyr, Viachaslau Hurmanau, Sam Cousins, Robin Hultgren, Jose Schroeder, Ched, Claustrophobya, Charles Wang, Dolan Dark, Casaro, Donglin Li, Sarah Thompson, Pamela Palmer, Fergal Harrington, Jonas Erath, Spencer, Zsuzsi Balai, Tyler Roberts, Allyssa Blalock, Robert Bishop, Carl-Johan Linde, Thomas Nielsen, Heather Pray, Marco Boneberger, Mehsotopes, Joe Johnston, ugo dubois, Keagan Boys, Miles Gard, Frantisek Sumsala, Scott, Tobias Theobald, Solar3ty Games, Nicholas Carr, K41N_of_2358, Daniel Rodrguez, Pixlpit, Gytis Kirvela, Thomas Flanigan, Dwagon, Costin Graur, Mavis Everett, Kwiatkowski Robert, Huo Benpeng, Dan Gretton, Joshua Davison, Bryce Comp, Andrey Lipattsev, DEFECT DAVIS, Gurleen Saini, Andrew "FastLizard4" Adams, Isak Hietala, Leon Han, Sarah Johnson, Kieran Chakravorty, Hanna Khoury, Kimberly Martin, Jon Glass, Julius Wroblewski, Ben Zautner, Kester Falge, Juan Florez, Tad Moore

Help us caption & translate this video!

http://www.youtube.com/timedtext_cs_p...

The Rise of the Machines Why Automation is Different This time

Here is the original post:

The Rise of the Machines Why Automation is Different ...

Marketing your small business, whether online or off, is a time intensive process. As your business grows, managinglead capture, nurturing, converting and relationship management become too big to handle manually, which is why small business owners turn to marketing automation software to managethe load.

In general, automationbrings many benefits to your small business including:

However, because the promotion, selling, and relationship management processes involve so many steps, manyof which arerepeated for each customer, marketing isparticularly suited to automation. And thats where marketing automation software comes in.

Happily, there are many marketing automation software options for small businesses. These solutionscan handle a few, or alltypes, ofautomated marketing techniques. In other words, they include various automation features such as:

Note:Not all solutions offer every feature.

While you may be hesitantto try one becauseofeither cost orlearning curve, you should be aware that many marketing automation software vendors:

Are you considering marketing automation for your small business? If so, heres a list of marketing automation software solutions to consider.

GetResponsecalls itself the all-in-one online marketing platform to grow your business and a look down the features on their home page shows just how complete the software is.

While its oneof the most affordable solutions on this list,GetResponse brings the same, if not more,of the features and functionalityoffered by the more expensive solutions on this list. That said, the price doesincrease with use, but a small business should be able to handle the increase as it grows.

Another affordable solution, ActiveCampaign,offers everything a small business needs to automate its marketing efforts including a robust, built-in CRM system.

Calling itself a small business CRM, GreenRope is almost a small business management suite. Starting withmarketing automation, youll find website tracking, landing pages and more in this affordably-pricedsolution.

GreenRopealso offers sales and operations functionality setting the tool apart.

One of the more well-known marketing automation software options, Infusionsoftoffers everything your small business needs at a reasonable price.One feature that shows off the power of this solution is the flexibility of the campaign builder. This tool enables you to create elaborate workflows one timeand then implement them again and again. These workflows can include many types of steps including eCommerce, appointments, behaviors and actions, webinar attendance and many more.

Additional Resources

The only tool on the list to offer a website builder, HubSpot aims to integrateyour entire marketing effort in one place. One of the more powerful features of the tool is the ability to personalize your website with smart content based on a number of factors:

Additional Resources

Act-Onoffers arobust marketing automation platform.The software offers automation workflows and triggers as well as website behavior tracking, integration with many popular CRM platforms, and more.

One of the moreinteresting, and useful, featuresof Act-On is its funnel reporting. By setting up a sales funnel, you can track the effectiveness of your overall marketing efforts. Heres a sample:

AdditionalResources

Marketo offers a powerful solution with many features. One thing that stood out however was theircustomizedproduct bundling, an approach that may makethe tool attractive for small businesses that want to dip their toe in the water.

One of the most interestingaspects of Autopilot is the number of integrations it enables you to use as part of your marketing automation workflows. For example, below you can seethat the bottom right step sends an automated Slack message:

In addition, the vendor offers multi-channel marketing via emails, headsups (little pop-upnotifications) SMS messages, and even postcards. Finally, the pricing for this solution is low and scales as your business grows.

Salesfusion is a heavily-loaded marketing automation toolthat can help you take your small business to a new level. One standout feature? Its SEO audit featurethat helps improve your search engine rankings.

In addition to its marketing automation features, SharpSpring offers additional features includinga blog builderandVisitorID tool which attempts to identify anonymous visitors to your website.

Also, the vendor enables you to use your buyer personas to automatically offer unique, targeted content by segmenting your customers based on how closely their profiles match.

Additional Resources

While a look at SALESmanagos home page may make you run, dont let the complexity of the vendors offerings chase you away. This solution literally has it all and, if thats what you need, then its certainly worth a look.

No matter whichmarketing automation software solution you select, make sure youre getting the most out of the tool. And remember, you can automate processes beyond marketing, too, so besure to consider how leveraging other tools can helpstreamline your small business.

Automated Marketing Photo via Shutterstock

More:

Take Your Business to the Next Level with Marketing Automation Software - Small Business Trends

Subscribe today for full access on your desktop, tablet, and mobile device.

61

Let friends in your social network know what you are reading about

Rockwell Automation President and Chief Executive Officer Blake Moret shared his companys successful workforce development strategies at a roundtable discussion at the White House Wednesday.

Try Another

Audio CAPTCHA

Image CAPTCHA

Help

CancelSend

A link has been sent to your friend's email address.

A link has been posted to your Facebook feed.

Blake Moret (left) succeeded Keith Nosbusch (right) as the CEO of Rockwell Automation last year.(Photo: Business Wire)

Rockwell Automation President and Chief Executive Officer Blake Moret shared his companys successful workforce development strategies at a roundtable discussion at the White House Wednesday.

The conference was presented in conjunction with the Business Roundtable and was led by Ivanka Trump, daughter and adviser of President Donald Trump, and Labor Secretary Alexander Acosta.

Moret was one of about 20 CEOs who shared their workforce development success stories with the White House staff.

Reached by telephone after the conference, Moret said he shared three successful staff development strategies that Milwaukee-based Rockwell deploys: acommitment to lifelong learning;outcome-based instruction;and partnerships between manufacturers and learning centers, such as technical colleges.

I expressed the three principles for having a skilled workforce, Moret said. These are the things that work.

Moret said he was impressed by Ivanka Trumps interest in the subject.

She was an active participant and is clearly knowledgeable about the subject, Moret said.

Ivanka Trump and her father traveled Tuesday to Wisconsin, where they toured an apprenticeship program at Waukesha County Technical College.

The Business Roundtable is an association of chief executive officers of leading U.S. companies.

Read or Share this story: http://jsonl.in/2stJgbg

Read this article:

Rockwell Automation CEO shares workforce development strategies at White House - Milwaukee Journal Sentinel

SAN FRANCISCO Nokias new super-fast chipgot all the love at the companys swanky unveiling event in San Francisco this week. The company said its FP4 silicon-based routers will power the IP networks of the future. But reaching the target fully automated networking requires powerful software.

These routers will provide a high-performance network platform, which when combined withNokias Deepfield big-data analyticsand automation with ourSDN [software-defined networking] solutions will deliver to the vision of insight-driven automated networking, wrote Nokia VP Manish Gulyani on a Nokia blog the day after the event.

Nokias not there yet. Were in the process, of automated networking, said Steve Vogelsang, CTO for Nokias IP and optical business. The companys software innovations will move networks closer to self-driving status, he said.

We have the SDN control layer so we can drive changes into the network, but now there are still human beings on top of that, Vogelsang said. We havent connected it to the analytics. The first step is to get better at the analytics.

Security automation will likely happen quicker than overall network automation. This is where Deepfields technology fits in by automating the process of looking for anomalies. In DDoS [distributed denial of service] you dont have time for a human to interact and thats why weve spent a lot of money with Deepfield, making sure we minimize false positives, Vogelsang said.

Nokia acquired Deepfield earlier this year. The analytics software startup mapped billions of IP addresses, with the insight put into a database called Cloud Genome. It also collects network telemetry from routers.

This information can be used to provide network insights and prevent DDoS attacks.

We patented technology that goes out and actually builds maps of the entire Internet at scale, every day, all day, explained Craig Labovitz, GM of Nokia Deepfield. We combine that with data, with visibility into where the traffic is going, and where the traffic has come from. All of this is being done in software, at scale, in the network.

This enables real-time security as it provides instant knowledge of every application on the network.

It also helps avoid false positives these occur when DDoS prevention software detects a surge in Internet traffic and wrongly thinks its an attack. Deepfields maps of the Internet help it determine if the surge is a legitimate increase in traffic or an actual DDoS attack.

Security is really about who has the best data? Who has the best data to address false positives? Can you discriminate the attackers from the legitimate traffic?, Labovitz said. With cloud and theInternet of Things (IoT) fueling major DDoS attacks, security cant be an afterthought, he added. Security is an integral part to the next-generation network.

Nokias Network Services Platform is its SDN platform for carriers. It allows operators to automate network services across multiple network layers, both on-premise and in the cloud. It also works with equipment from multiple vendors.

It is really unleashing the ability for full centralized control of the whole network, said Sasa Nijemcevic, VP and GM for network and service management at Nokia. A product like Deepfield adds another dimension to this story. We believe that most of the building blocks to achieve that automation of the network are there.

ACG Research analyst Stephen Collins uses a slide (below) to show how the software and hardware work together to automate the network.

The FP4 chip generates the network telemetry data to provide real-time visibility into packet flows, he wrote in an email. The Deepfield software handles the big data analytics and is a key part of the complete solution for generating the intelligence (insights) that closes the feedback loop for the orchestration software, which in Nokias world is the NSP network services platform.

The next-step is identifying use cases for automated networking, such as using Deepfield software to automate the process of looking for anomalies, ACG Research analyst and CEO Ray Mota said. Nokias top SDN vendor status means service providers trust the company to run the brains of the network, Mota added.

By having the credibility of becoming an incumbent, Nokia is getting the credibility to start setting the framework for automation, Mota said. You use the term self-driving car Im not going to get in a car and take my hands off the wheel thats automated from a vendor I dont trust.

Photo: Basil Alwan, president, IP/Optical Networks at Nokia, announces the newFP4 chip.

Jessica is a Senior Editor, covering next-generation data centers and security, at SDxCentral. She has worked as an editor and reporter for more than 15 years at a number of B2B publications including Environmental Leader, Energy Manager Today, Solar Novus Today and Silicon Valley Business Journal. Jessica is based in the Silicon Valley.

View original post here:

Nokia's Software is Key to Insight-Driven Automated Networking - SDxCentral

The new home for Visual Studio documentation is Visual Studio 2017 Documentation on docs.microsoft.com.

The latest version of this topic can be found at Automation.

Automation (formerly known as OLE Automation) makes it possible for one application to manipulate objects implemented in another application, or to expose objects so they can be manipulated.

An Automation server is an application (a type of COM server) that exposes its functionality through COM interfaces to other applications, called Automation clients. The exposure enables Automation clients to automate certain functions by directly accessing objects and using the services they provide.

Automation servers and clients use COM interfaces that are always derived from IDispatch and take and return a specific set of data types called Automation types. You can automate any object that exposes an Automation interface, providing methods and properties that you can access from other applications. Automation is available for both OLE and COM objects. The automated object might be local or remote (on another machine accessible across a network); therefore there are two categories of automation:

Automation (local).

Remote Automation (over a network, using Distributed COM, or DCOM).

Exposing objects is beneficial when applications provide functionality useful to other applications. For example, an ActiveX control is a type of Automation server; the application hosting the ActiveX control is the automation client of that control.

As another example, a word processor might expose its spell-checking functionality to other programs. Exposure of objects enables vendors to improve their applications by using the ready-made functionality of other applications. In this way, Automation applies some of the principles of object-oriented programming, such as reusability and encapsulation, at the level of applications themselves.

More important is the support Automation provides to users and solution providers. By exposing application functionality through a common, well-defined interface, Automation makes it possible to build comprehensive solutions in a single general programming language, such as Microsoft Visual Basic, instead of in diverse application-specific macro languages.

Many commercial applications, such as Microsoft Excel and Microsoft Visual C++, allow you to automate much of their functionality. For example, in Visual C++, you can write VBScript macros to automate builds, aspects of code editing, or debugging tasks.

One difficulty in creating Automation methods is helping to provide a uniform "safe" mechanism to pass data between automation servers and clients. Automation uses the VARIANT type to pass data. The VARIANT type is a tagged union. It has a data member for the value (this is an anonymous C++ union) and a data member indicating the type of information stored in the union. The VARIANT type supports a number of standard data types: 2- and 4-byte integers, 4- and 8-byte floating-point numbers, strings, and Boolean values. In addition, it supports the HRESULT (OLE error codes), CURRENCY (a fixed-point numeric type), and DATE (absolute date and time) types, as well as pointers to IUnknown and IDispatch interfaces.

The VARIANT type is encapsulated in the COleVariant class. The supporting CURRENCY and DATE classes are encapsulated in the COleCurrency and COleDateTime classes.

AUTOCLIK Use this sample to learn Automation techniques and as a foundation for learning Remote Automation.

ACDUAL Adds dual interfaces to an Automation server application.

CALCDRIV Automation client application driving MFCCALC.

INPROC Demonstrates an In-Process Automation server application.

IPDRIVE Automation client application driving INPROC.

MFCCALC Demonstrates an Automation client application.

MFC COM

View original post here:

Automation - msdn.microsoft.com

A significant percent of CIOs and other IT decision-makers consider digital transformation "Topic A" on their technology agenda, according to a recent survey from BMC. Successful transformations are currently creating new sources of revenue, improving operations and establishing unique competitive advantages. The automation of manual processes, however, is essential. In fact, nearly all the survey respondents believe that technology and automation will spread from IT to all areas of the business by 2020 to "transform everything." Many strongly agree that businesses that fail to embrace IT automation as a driver of digital businesses won't even exist 10 years from now. Fortunately, the vast majority of survey respondents said their organization has all the resources required to continue innovating technology to reach their goals, with strong alignment between lines of business (LoBs) and the IT department. And most have embraced DevOps as a means to help teams complete projects and achieve strategic objectives. More than 650 global IT decision-makers took part in the research.

Dennis McCafferty is a freelance writer for Baseline Magazine.

Here is the original post:

Automation Is Vital for IT Transformation - CIO Insight

In Brief The outgoing CEO of General Electric spoke at a tech conference in Paris where he called the idea of widespread automation in the next five years "bulls***." He believes that a lack of tech executives experience in a factory leaves them unqualified. Stunted Revolution

Most executives in tech believe that the next five years will bring about a significant number of jobs lost to automation. As advances in robotics and artificial intelligence (AI) are being rapidly developed, the capability of machines to do work previously requiring humans is ramping up. However, not all executivessubscribe to this idea of the ultra-fast progression of automation.

Outgoing Chief Executive of General Electric, Jeff Immelt did not mince words regarding his feelings about the impending automation take over. Speaking at the Viva Teach conference in Paris, Immelt said, I think this notion that we are all going to be in a room full of robots in five years and that everything is going to be automated, its just BS. Its not the way the world is going to work.

Immelt believes that tech executives who have no experience running or working in a factory have no idea of how they actually operate and therefore cannot realistically gauge how automation will progress.

Other experts like tech giant Elon Musk and Greg Creed, the CEO of Yum Brands (the people behind Pizza Hut, KFC, and Taco Bell) believe in the near threat of automation to many human jobs. Elon Musk goes even further in saying that humans need to integrate with machines in order to remain relevant in the future.

The problem with looking at automation as something in the far off future is that it limits the necessary conversations of what we can do to prepare workers for job losses. One of the more popular solutions to this automation issue is a Universal Basic Income (UBI) that is supported by the likes of Musk, Mark Zuckerberg, and other experts.

Both sides of this issue are interpreting evidence into predictions. These predictions can only be discounted or vindicated by time. Even so, the questions of what we can do to prepare are still vital whether automation is 5 or 50 years away.

Here is the original post:

GE CEO Says Automation Within the Next 5 Years is Not Realistic - Futurism

Key Takeaways

This article first appeared inIEEE Softwaremagazine.IEEE Softwareoffers solid, peer-reviewed information about today's strategic technology issues. To meet the challenges of running reliable, flexible enterprises, IT managers and technical leads rely on IT Pro for state-of-the-art solutions.

Robots are widely used for many repetitive tasks. Why not software testing? Robotic testing could give testers a new form of testing thats inherently more black-box than anything witnessed previously. Toward that end, we developed Axiz, a robotic-test generator for mobile apps. Here, we compare our approach with simulation-based test automation, describe scenarios in which robotic testing is bene cial (or even essential), and tell how we applied Axiz to the popular Google Calculator app.

Robotic testing can address the profound shift1,2 from desktop to mobile computation. This trend is projected to gather steam,3 accelerated by a concomitant shift from desktop to mobile-device ownership. Automated software testing is needed more than ever in this emerging mobile world. However, we might need to rethink some of the principles of software testing.

Mobile devices enable rich user interaction inputs such as gestures through touchscreens and various signals through sensors (GPS, accelerometers, barometers, neareld communication, and so on). They serve a wide range of users in heterogeneous and dynamic contexts

such as geographical locations and neworking infrastructures. To adequately explore and uncover bugs, testing must be able to take into account complex interactions with various sensors uder a range of testing contexts. A suvey of mobile-app development indcated that practical mobile-app testing currently relies heavily on manual tesing, with its inherent inef ciencies and biases.4 Frameworks such as Appium (appium.io), Robotium (github.com /RobotiumTech/robotium), and UIAtomator (developer.android.com/topic /libraries/testing-support-library/index .html#UIAutomator) can partly support automatic test execution. However, they rely on human test script design, thereby creating a bottleneck.

Fortunately, many advances in atomated Android testing research have recently occurred.58 However, these techniques use intrusive (partly or fully white-box) approaches to execute the generated test cases. They also assume that testing tools will enjoy developelevel permissions, which isnt always the case.

Many such techniques need to modify the app code or even the mobile OS, while even the most black-box of approaches communicate with the app under test (AUT) through a test harness. This isnt truly black-box because it relies on a machine-to-machine interface between the test harness and AUT.

A truly black-box approach would make no assumptions, relying only on the device-level cyber-physical iterface between the human and app. Testing at this abstraction level also more closely emulates the experience of real users and thus might yield more realistic test cases. Furthemore, such an approach is inherently device independent, a considerable benefit in situations that might ivolve more than 2,000 different dvices under test.9

Handheld devices require rethinking what black-box testing really means. Their user experience is so different from that of desktop applications that existing machine-to-machine black-box test generation lacks the realism, usage context sensitivity, and cross-platform exibility needed to quickly and cheaply generate ationable test cases.

This section sets out a manifesto for robotic testing in which the geerated test cases execute in a truly black-box (entirely nonintrusive) manner. Table 1 compares manual, simulation-based, and robotic testing.

For Android testing, MonkeyLab generates test cases based on app usage data.10 Researchers have also published several approaches to geerating realistic automated test input for web-based systems.11 However, these automated test-input-based systems dont target mobile plaforms, and the overall body of lierature on automated test input geeration has paid comparatively little attention to test case realism.

A developer wont act on a test sequence that reveals a crash if he or she believes that the sequence is unrealistic. Also, all automated test data generation might suffer from unrealistic tests owing to iadequate domain knowledge. Mbile computing introduces an aditional problem: a human simply might not be able to perform the tests. For example, they might require simultaneous clicking with more than five fingers.

In comparison, a robotic test harness can physically simulate hman hand gestures. Although there might be some human gestures a robot cant make (and others that a robot can make but no human can replicate), the robotic gestures will at least be physical gestures. As such, those gestures will be closer to true human interaction than the virtual gestures simulated by current nonrbotic test environments, which siply spit a generated sequence of events at the AUT.

Existing white-box and (claimed) black-box automated testing rquires modifying the behavior of the AUT, the platform, or both. Even techniques regarded as blacbox communicate with apps though simulated signals rather than signals triggered through real sensors (for example, touchscreens or gravity sensors) on mobile devices.

As we mentioned before, robotic testing uses the same cyber-physical interface as the human user. Its also less vulnerable to changes in the underlying platform, API intefaces, and implementation details. In a world where time to market is critical, the ability to quickly dploy on different platforms is a cosiderable advantage.

Human-based testing is consideably expensive yet enjoys much ralism and device independence. In contrast, current automated test data generation is relatively inexpensive, relying only on computation time, yet it lacks realism and device indpendence. Robotic testing seeks the best costbene t ratio and combines the best aspects of human-based testing and machine-to-machine atomated testing.

Although robotic technology has historically proven expensive, were witnessing a rapid decrease in robotic technologys cost. Crowsourcing, too, is reducing the cost of human-based testing12 but is ulikely to ultimately be cheaper than robotic testing.

Traditional automated testing makes a number of assumptions about the system under test, whereas humabased test data generation makes fewer assumptions. Robotic testing is much closer to human-based tesing in the number of assumptions made, yet its ability to generate large numbers of test cases cheaply is much closer to existing autmated testing.

Figure 1 shows the Axiz architeture, which contains two high-level components: the robotic-test genertor and robotic-test executor.

The robotic-test generator analyzes the AUT and uses the extracted iformation (including app categories, static strings, and APIs) to adjust a realism model. This model uses prviously collected empirical data cotaining known realistic test cases.

Tabel 1:Criteria to consider when choosing manual, simulation-based, or robotic testing

On the basis of observations of human usage, we compute a coprehensive list of properties (for eample, the delay between two adja-cent events, event types, and event patterns) that capture the underlying real-world test cases characteristics and properties. We hope these characteristics capture what it is to be ralistic, so that Axiz can use them to guide and constrain automated test data generation.

Figure1. The architecture of the Axiz robotic-testing system. The robotic-test generator generates realistic tests. The robotic-test executor lters out unexecutable tests and executes the rest.

The robotic-test generator passes the realism model and AUT to the evolutionary-search component, which generates and evolves test cases. These test cases realism derives from two aspects of our approach. First, by reusing and extending ralistic test cases (for example, Rbotium or Appium test scripts), we draw on previous tests manually written by the app testers. Second, by searching a solution space costrained by the realism model, we focus on generating test cases that meet the constraints identi ed ealier from crowdsourced tests.

We evaluate the generated test cases tness on the basis of their performance (such as code coverage and fault revelation) and realism as assessed by the realism model.

We further validate the test case cadidates by executing them on a physcal device so that they interact with itin much the same way users or maual testers might do. The robotitest executor translates the coded test scripts into machine-executable commands for the robot and then eecutes them on a robotic arm.

The arm interacts with the mbile device nonintrusively, just as a human would. This process requires inverse kinematics and calibration components to make the manipultor act accurately. A camera montors the mobile-device states. The robotic-test executor further prcesses image data from a camera through computer vision techniques, which perform object detection and oracle comparison.

Finally, the robotic-test executor sends the overall process data logged during the execution process to the test lter to determine whether the candidate test case is executable in a real-world setting. If not, the executor filters it out. Otherwise, Axiz saves the test for reuse.

We implemented a prototype of Axiz to demonstrate the systems feasibiity (see Figure 2). We built our implementation entirely from commoity hardware components, which are inexpensive, widely available, and interchangeable. We use 3D visiobased self-calibration13 to help calbrate and adjust the robotic maniulator to keep the system working reliably and to serve as input to the oracle comparator.

The manipulator is a four-axis Arduino-based robotic arm. Its driven by stepper motors with a psition repeatability of 0.2 mm. The maximum speed of movement for each axis ranges from 115 to 210 dgrees per second (when loaded with a 200-g load, a sufficient maximum for most mobile devices). At the arms end is a stylus pen that simlates nger-based gestures.

An external CMOS 1,080-pixel camera monitors the test execution. We run the test generator and robot controller on a MacBook Pro laptop with a 2.3-GHz CPU and 16 Gbytes of RAM.

We employ inverse kinematics (in Python) for robotic-arm control. The object detector and oracle compartor are implemented on top of the OpenCV library. The robotic-test generator employs NSGA-II (Nodominated Sorting Genetic Algorithm II), a widely used multi-objective gnetic algorithm, for multi-objective search-based software testing, uing our (currently state-of-the-art) tool Sapienz.8 This tool generates sequences of test events that achieve high coverage and fault revelation with minimized test sequence length.

The Google Calculator app has had5 to 10 million installs.14 Although its simple, its a nontrivial real-world app and thus illustrates the potential for truly black-box robotic testing.

We used the robotic-test genertor to generate realistic tests, which we executed using the robotic mnipulator. The device under test was a Nexus 7 tablet, with normal user pemissions and the of cial Android OS (without modi cation). For comparson, we introduced another Nexus 7 on which we allowed more tradtional intrusive testing. The second Nexus 7 was directly connected to the robot controller on the MacBook. The test tool for it had developer-level privileges and could modify the OS.

Figure 3 illustrates this process. The MacBooks interpreter compnent translated the event instructions into motion speci cations for the robotic-arm controller. That cotroller then transformed the specifications into joint angle instructions on the basis of inverse kinematics. As Figure 3 shows, the robotic arm touched the buttons on the rst Nexus 7 to perform testing. The oacle comparator witnessed each test event. After each step of the test eecution, it captured images through the external camera and validated the mobile-GUI states.

Axiz accurately executed each test event speci ed in the generated robotic-test cases and passed the rquired oracle checkpoints, faithfully maximizing Sapienzs abilities.

Figure2. Testing mobile apps with a four-axis robotic arm. We built our implementation entirely from commodity hardware components, which are inexpensive, widely available, and interchangeable.

Avideo of Axiz perforing this testing is here.In it, we demostrate Axiz side by side with a trditional automated-testing tool that doesnt use a robot arm but simply produces a sequence of events. Thevideo demonstrates that the robotic arm, built from cheap commodity hardware, can physically produce the same set of events, but more ralistically, thereby achieving greater device independence and realism.

We thank Andreas Zeller for his invited talk at the 36th CREST (Centre for Rsearch on Evolution, Search and Testing) Open Workshop,15 during which he prsented a playful video of a disembodied synthetic human hand automatically iteracting with a mobile device. This was one of the inspirations for our research.

KeMaois a research student at the Centre for Research on Evolution, Search and Testing (CREST) at University College London. Contact him at k.mao@cs.ucl.ac.uk.

MarkHarmanis the director of the Centre for Research on Evolution, Search and Testing (CREST) at University College London. Contact him at mark.harman@ucl.ac.uk.

YueJiais a lecturer of software engineering at the Centre for Research on Evolution, Search and Testing (CREST) at University College London. Contact him at yue.jia@ucl.ac.uk.

This article first appeared inIEEE Softwaremagazine.IEEE Softwareoffers solid, peer-reviewed information about today's strategic technology issues. To meet the challenges of running reliable, flexible enterprises, IT managers and technical leads rely on IT Pro for state-of-the-art solutions.

Here is the original post:

Robotic Testing of Mobile Apps for Truly Black-Box Automation - InfoQ.com