Mobile Application Testing – 01 Synergy

April 4th, 2012 by Rahul No comments »

01 Synergy offers a complete and comprehensive range of Mobile Application testing services from Unit Testing to User Acceptance Testing. Complexities across handset makers, carriers, locations and operating systems has made building bug-free mobile apps really difficult.

Our areas of expertise include:

  • Requirements Capture and Analysis
  • Test Planning
  • Test case Design
  • Test Execution
  • Defect Tracking & Management
  • Reporting
  • Test Metrics

01 Synergy offers a wide range of Mobile Application testing services, including:

  • Functional Testing
  • Security Testing
  • Load & Performance Testing
  • Localization Testing
  • Usability Testing

Our QA professionals can help you with all your Mobile App testing projects,  including:

  • iOS Application Testing (iPhone, iPad, iPod Touch)
  • Android Application Testing
  • BlackBerry Application Testing
  • Windows Phone 7 Application Testing

01 Synergy is here to help, if you have a need to discuss Mobile application testing, agile testing, do count on us to help. Visit us online at or send us a mail here:

RIB Software ersetzt PSI im TecDax

September 22nd, 2014 by Amrinder No comments »

Motorola Moto G 2014 which is priced at $179 at Amazon is praised for its amazing specs at reasonable price. Well, the Asus Zenfone 5 is also a marvelous smartphone that come with awesome features with similar price tag. Both the handsets support dual SIM cards.

We have compared the two mid-range smartphones on the basis of the specifications ilsted with PhoneArena to let you know the Moto G 2014 already has a lesser-known serious competitor


The Moto G measures 5.57 x 2.78 inches and weighs 149 g. The Zenfone 5 measures 5.83 x 2.87 inches with a weight of 145g.

As far as thickness is concerned, the new Moto G measures 11 mm and Zenfone 5 measures 10.3 mm.

The Moto G comes with a splash resistant body and comes with dual front-facing speakers.

The Zenfone 5 is available in colours like black, white, red, gold and purple. The Moto G is only available in two colours of black and white but the powerful Moto Maker customization feature allows the user to replace the back panel with covers of vibrant designs and colours.


Both the handsets support HD resolution of 720 x 1280 pixels and feature a 5-inch IPS LCD displays that support 294 ppi.

Processor, RAM and Internal Storage

The Moto G 2014 is powered with Snapdragon 400 chipset that come with 1.2 GHz quad-core processor along with Adreno 305 graphics. It features 1 GB RAM and the external storage supports up to 32 GB of microSD.

Asus has packed the Zenfone 5 is available in two CPU variants. The Intel Atom Z2580 variant is the 16 GB edition which comes with 2 GB RAM. The Intel Atom Z2560 is the 8 GB edition that is equipped with 1 GB RAM. The Zenfone 5′s external storage supports a higher capacity of up to 64 GB of microSD.

Camera and Software

The Moto G and Zenfone 5 smartphones come with 8MP camera along with features like autofocus and LED flash. The Moto G’s camera only supports 720p HD video recording whereas the camera sensor Zenfone 5 supports full HD 1080p video recording. Also, both the handsets are housed with 2MP front-facing cameras.

On the software front, Moto G is available with the latest Android v4.4.4 KitKat OS and if it is also in line to receive the soon-to-arrive Android L update. The Android OS on Moto G is devoid of any UI and its present in its purest form.

The Asus Zenfone 5 has an older version of Android, the v4.3 JellyBean edition. It can be upgraded to v4.4.2 KitKat. The Android OS on both the handsets are overlaid with proprietary UI from respective manufacturers.

Connectivity and Battery

Moto G and Zenfone 5 come with common connectivity features like 3G, Wi-Fi 802.11 b/g/gn, microUSB 2.0, v4.0 Bluetooth, aGPS and GLONASS. The Moto G 2014 does not feature LTE connectivity. The Zenfone 5 LTE edition supports 4G LTE connectivity.

The Motorola Moto G has a non-removable battery of 2070 mAh capacity. The Asus Zenfone 5 too features a non-removable battery of slightly larger capacity of 2110 mAh size.


Micro Focus Acquires Attachmate, Builds Software Powerhouse

September 22nd, 2014 by Amrinder No comments »

Two software powerhouses with extensive software portfolios aimed at proprietary IBM systems are coming together with Micro Focus shelling out $1.2 billion and assuming a bunch of debt to acquire Attachmate. The combination will create a software giant with just under $1.4 billion in sales and 34,000 customers between the two of them.

Micro Focus, which is perhaps best known for its COBOL application development tools, is oddly enough the smaller of the two companies, so it might seem unusual for Micro Focus to be doing the buying. But it was always assumed that the private equity owners who had assembled the Attachmate conglomerate over the past decade would want to cash out someday and in a big way as they have done with this deal. Those equity partners include Golden Gate Capital, which owned a majority stake in Micro Focus after its initial public offering back in 2005, and was the biggest shareholder in Attachmate with a 31.5 percent piece. (The company divested its Micro Focus holdings in 2009.) Francisco Partners has a 29.9 percent stake of Attachmate, followed by Thoma Bravo with 14.1 percent, Elliot Management with 13.2 percent, and management and others with 11.3 percent. After the deal is done, these combined equity partners will have a 40 percent stake in the company, which will be Micro Focus and which will still have its shares traded on the London Stock Exchange.

In its most recent incarnation, Attachmate is privately held. The private equity partners bought Attachmate, predominantly a supplier of terminal emulation software for proprietary systems like IBM mainframes and midrange gear, and combined it with rival WRQ back in 2004. Then this company bought NetIQ, a provider of security software, in 2006 and five years later it acquired NetWare and SUSE Linux software maker Novell for $2.2 billion. Novell had $1 billion in cash, so Attachmate did not pay as much as you think for Novell. Micro Focus is paying more than it might appear to get control of Attachmate. The equity partners are getting $1.18 billion in shares of Micro Focus and the COBOL tool maker is assuming $1.16 billion in debt and other charges (including payouts to equity partners) for a total value of $2.35 billion. This is a fairly large chunk of change for Micro Focus, but the deal will triple its revenues and boost its earnings before income taxes by around a factor of 2.6. There is no way that Micro Focus could accomplish such a feat with its own product lines or by doing a series of smaller acquisitions.

The amazing thing about both Micro Focus and Attachmate is that they have very large customer bases and they both get the lion’s share of their sales each year from recurring revenue streams such as maintenance and subscriptions. A relatively small portion of their sales come from professional services, and license revenues for the combined company represented about a quarter of the revenue stream in a hypothetical fiscal 2014 that ended this spring. (Micro Focus ends its fiscal year in April, Attachmate ends it in March.) In this fiscal 2014, the combined companies derived 70 percent of revenues from recurring deals, not license sales or professional services.

“This is a transformational deal for Micro Focus,” explained Kevin Loosemore, executive chairman of Micro Focus, in a statement in a regulatory filing going over the deal. “The merger presents a rare opportunity to create a leading infrastructure software company with the scale and breadth to compete successfully at a global level. It provides us with a platform from which I am confident we can deliver significant and sustainable returns.”

As you can see, the Visual COBOL, enterprise application modernization tools, and the Rumba terminal emulators represent the largest portion of revenues at Micro Focus and together they have about 8,000 customers and accounted for $309 million. (That’s the purple and blue parts of the columns above added together.) The company acquired application development tool maker Borland five years ago for $75 million, and this business has about 2,000 customers and brought in $65 million, Various other tools make up the remainder of the Micro Focus revenue stream and have about 1,400 customers. The core COBOL development business is shrinking a bit–1 percent in fiscal 2014–and the other units are growing.

Attachmate, by contrast, saw its core host connectivity business contract by 8 percent in fiscal 2014, to $186 million. This includes the Reflection, InfoConnect, Verastream, and DataBridge product lines, which together have around 4,000 customers worldwide.

The Novell business, which includes NetWare and its Open Enterprise Server hybrid with SUSE Linux as well as GroupWise collaboration and ZENworks file and network endpoint management tools, accounted for $285 million in revenues, but shrank by 12 percent last fiscal year. NetIQ application and security management software brought in $289 million in revenues and grew 1 percent, and SUSE Linux, thanks to help from the supercomputing and SAP HANA markets, grew by 9 percent to $197 million last year. Red Hat does not break out Enterprise Linux as a separate line item in its financials, but the bulk of its revenues come from Linux support contracts even to this day and it is probably selling between four and five times as much Linux support contracts as SUSE Linux.

Add it all up, and this much-embiggened Micro Focus has been shrinking a tiny bit over the past three years but managing to keep earnings before income taxes, depreciation, and amortization more or less flat. The company is among the top three vendors in most of its product categories–it is more distant from the front of the pack in collaboration, identity management, and security tools, but these are also crowded markets.

Under the deal that Micro Focus is proposing, it will retire $1.56 billion in existing debt from both companies (with just under $1.3 billion coming from Attachmate), give $136.2 million to Micro Focus shareholders as a deal sweetener, and take on $1.85 billion in new debt plus another $150 million in a revolving credit line. Bank of America Merrill Lynch, HSBC, RBC Capital Markets, Goldman Sachs, and Credit Suisse are ponying up the money. That debt is equivalent to a year and a third of revenues, and it is substantially larger than the mere $266.2 million that Micro Focus was carrying on its books (and that still represented more than half a year’s revenue). Micro Focus has committed to chopping that debt load by somewhere in the neighborhood of $500 million in the next two years after the deal closes.

Micro Focus stock jumped 17 percent in the days following the announcement of the deal, giving it a market capitalization of $1.43 billion, so Wall Street and the City of London both seem to think this deal is a good idea despite the debt load. Breadth and depth are more important than debt, apparently.

The plan for the Attachmate deal going forward is to get the new debt facilities secure by early October, when a prospectus will also be published. Micro Focus expects to have a general meeting before the end of October to vote on the deal and to close it in early November. The deal is obviously expected to be accretive to Micro Focus earnings in fiscal 2015. During that fiscal year, Micro Focus will do a detailed review of the combined businesses and “invigorate product management,” as the company put it. In fiscal 2016, Micro Focus will be rationalizing its legal entities and product lines, standardizing the systems that run its business, and come up with a new go-to-market model. Micro Focus has more than 1,200 employees and Attachmate has more than 3,300 and presumably there will be some redundancies here, too. The idea is to stabilize the top line in fiscal 2017 and boost profits and then actually grow the top line (and presumably profits, too) in fiscal 2018.


Robots That Learn Through Repetition, Not Programming

September 22nd, 2014 by Amrinder No comments »

Eugene Izhikevich thinks you shouldn’t have to write code in order to teach robots new tricks. “It should be more like training a dog,” he says. “Instead of programming, you show it consistent examples of desired behavior.”

Izhikevich’s startup, Brain Corporation, based in San Diego, has developed an operating system for robots called BrainOS to make that possible. To teach a robot running the software to pick up trash, for example, you would use a remote control to repeatedly guide its gripper to perform that task. After just minutes of repetition, the robot would take the initiative and start doing the task for itself. “Once you train it, it’s fully autonomous,” says Izhikevich, who is cofounder and CEO of the company.

Izhikevich says the approach will make it easier to produce low-cost service robots capable of simple tasks. Programming robots to behave intelligently normally requires significant expertise, he says, pointing out that the most successful home robot today is the Roomba, released in 2002. The Roomba is preprogrammed to perform one main task: driving around at random to cover as much of an area of floor as possible.

Brain Corporation hopes to make money by providing its software to entrepreneurs and companies that want to bring intelligent, low-cost robots to market. Later this year, Brain Corporation will start offering a ready-made circuit board with a smartphone processor and BrainOS installed to certain partners. Building a trainable robot would involve connecting that “brain” to a physical robot body.

The chip on that board is made by mobile processor company Qualcomm, which is an investor in Brain Corporation. At the Mobile Developers Conference in San Francisco last week, a wheeled robot with twin cameras powered by one of Brain Corporation’s circuit boards was trained live on stage.

In one demo, the robot, called EyeRover, was steered along a specific route around a chair, sofa, and other obstacles a few times. It then repeated the route by itself. In a second demo, the robot was taught to come when a person beckoned to it. One person held one hand close to the robot’s twin cameras, so that EyeRover could lock onto it. A second person then maneuvered the robot forward and back in synchronization with the trainer’s hand. After being led through a rehearsal of the movements just twice, the robot correctly came when summoned.

Those quick examples are hardly sophisticated. But Izhikevich says more extensive training conducted over days or weeks could teach a robot to perform a more complicated task such as pulling weeds out of the ground. A company would need to train only one robot, and could then copy its software to new robots with the same design before they headed to store shelves.

Brain Corporation’s software is based on a combination of several different artificial intelligence techniques. Much of the power comes from using artificial neural networks, which are inspired by the way brain cells communicate, says Izhikevich. Brain Corporation was previously collaborating with Qualcomm on new forms of chip that write artificial neural networks into silicon (see “Qualcomm to Build Neuro-Inspired Chips”). Those “neuromorphic” chips, as they are known, are purely research projects for the moment. But they might eventually offer a more powerful and efficient way to run software like BrainOS.

Brain Corporation previously experimented with reinforcement learning, where a robot starts out randomly trying different behaviors, and a trainer rewards it with a virtual treat when it does the right thing. The approach worked, but had its downsides. “Robots tend to harm themselves when they do that,” says Izhikevich.

Training robots through demonstration is a common technique in research labs, says Manuela Veloso, a robotics professor at Carnegie Mellon University. But the technique has been slower to catch on in the world of commercial robotics, she says. The only example on the market is the two-armed Baxter robot, aimed at light manufacturing. It can be trained in a new production line task by someone manually moving its arms to direct it through the motions it needs to perform (see “This Robot Could Transform Manufacturing”).

Sonia Chernova, an assistant professor in robotics at Worcester Polytechnic Institute, says that most other industrial robot companies are now working to add that type of learning to their own robots. But she adds that training could be tricky for mobile robots, which typically have to deal with more complex environments.

Izhikevich acknowledges that training a robot via demonstration, while faster than programming it, produces less predictable behavior. You wouldn’t want to use the technique to ensure that an autonomous car could detect jaywalkers, for example, he says. But for many simple tasks, it could be acceptable. “Missing 2 percent of the weeds or strawberries you were supposed to pick is okay,” he says. “You can get them tomorrow.”


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