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CIOZone Experts
Opinions and views from expert CIOZone members.
You may have seen my previous post on why understanding the need for mobile testing is a critical element of staying on top of the evolving mobile landscape. As a refresher, I explained the differences between mobile and traditional testing – the major one being to accommodate the near-constantly changing platforms and architectures. It’s a tough world out there in the application development universe, as companies strive to create products that work across as many as 1,800 different platforms and the latest operating systems. In this follow-up post, I’ll explain the benefits of using reliable testing platforms to maintain your applications and ensure reliability and speed.
In our instant gratification society, our devices must work – and work fast. Websites should load quickly, even
on the go. The performance testers’ maxim – ‘seven seconds can cost you $7 million dollars’ – illustrates perfectly how a delay in performance can impact the bottom line. A major challenge within all testing organizations is the reuse and maintenance of testing scripts. Testers are all about efficiency, so repeatedly recreating test scripts negates most productivity gains. In traditional software testing, the challenge for test automation tools was to ensure that scripts were portable, easily editable, shared, versioned, and could be rolled back either natively or via a file/source control system – a challenge met by the evolution of most software testing tools. When it comes to mobile, the immaturity of the tools in the market means many scripts aren’t reusable across multiple platforms, devices, and application types. This is why organizations looking for a mobile test automation platform should factor in the overall maintenance of the scripts and tests being created. The mobile test automation tool should be able to reuse many scripts to test across multiple units – an important consideration bearing in mind the proliferation of devices. This script will be responsible for recording, replaying, modifying, and creating error controls on all test commands. The testing tool is the conduit between the connected devices and the extension inside a chosen IDE, such as Visual Studio. Done properly with the right tool and architecture, one test script will execute against multiple devices. Performance is everything. As mobile networks spread, so
does the need for robust application performance – as the risk of high-profile failures increases exponentially. The demand for accurate mobile application distribution across multiple devices and platforms raises the stakes for mobile development teams until performance testing becomes just as critical (if not more critical) as functional testing in an application’s release. While many organizations’ current infrastructure and performance
testing tools are sufficient, some fail to prepare for multi-channel/multi-speed swarms to the network during heavy load times. Infrastructures can withstand hyper-fast networks but unknowns quickly become apparent when traffic from slower networks hits the primary route. Issues such as open sockets resulting from a cell tower switchover and database pooling sharing load between users on fiber, to name a few, all require a fix. One way to simplify mobile testing is to use an infrastructure that allows for developers to write unit tests using open source frameworks such as JUnit. Working with tooling that takes care of the ‘heavy lifting’ across different devices will shrink testing cycles. In this ecosystem, a business analyst can design the tests with visual tooling, export them as self-contained scripts that self-describe the given states, and then have them automatically generate JUnit code, which lets developers work as developers and testers to work as testers. Does it Load Better? Another often overlooked item within mobile testing is time and quality of application response under heavy loads. Is the user seeing the right screen? Is the application showing the correct ads under the load? What about HTML5 template based applications -- is the right style sheet showing up for the
right device? Because there have
been at least eight different versions
of the Android operating system
(OS) since 2009, performance and functional testing of different Android OS versions on multiple mobile devices is difficult. Add in iOS, bada, BlackBerry, Windows Mobile, and HTML5 and testing complexity reaches another level. Testers must use tooling that has mobile performance simulation capabilities on top of having robust object recognition technology for functional testing. Mobile testers should also prepare for the future by practicing ‘edge testing’. Start with the popular, most common devices, typically the ones promoted by the major mobile phone providers (i.e. iPhone, iPad, Google Nexus, Galaxy Tab etc.) to narrow the gap when developing a strategic plan of attack for mobile test automation. Then, test the smallest and largest screen sizes to ensure ample coverage. Edge testing with robust tools that allow for performance and functional testing is a strategy that still holds water, regardless of how often devices change. Do You Work Better? Clearly, with developers under pressure to deliver more robust code that can meet all these challenges, developers are going to have to work better and find more productive working practices
to create an improved product. Agile testing certainly ticks a lot of these boxes. Instead of a labor-intensive process where developers work in isolated teams and test software until destruction to find the bugs, Agile testing is helping teams work together to ensure the bugs don’t get in there in the first place. In other words, testers are writing automated tests to show that the software works, rather than obsessing about its vulnerability. Of course, Agile testers will still find bugs, and pre-Agile testers must write tests to show what works. But, Agile has prompted a more fluid and interactive way of thinking. Using tools that enable teams to collaborate out of the box is imperative to working better. The future of mobile testing isn’t – and cannot be – about silos of developers working on niche projects that seek to find problems. It’s going to be teams of developers and testers working closer together and drawing on their creativity and expertise to create robust code that meets the challenges of mobile. Once you have this component, along with an understanding of the testing process, you’ll be developing (and testing) awe-inspiring applications in no time! - Archie Roboostoff, Director, Borland Portfolio at Micro Focus
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Posted by adine in Untagged
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Posted by adine in Untagged
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Posted by Bill Gerneglia in Untagged
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When organizations look to reduce costs by following an outsourcing strategy, it’s easy to think about reducing capital expenditures by avoiding purchases of hardware, software licenses and networking bandwidth. But IT executives and their finance colleagues often believe it’s even more important to use outsourcing to reduce operating expenses, because those costs usually recur and often increase over time even without increases in business activity.
By Michael Salsburg, chief architect, Cloud Solutions, Unisys
Although we often talk about “the cloud” as if it were a homogenous entity, private cloud computing is very different from public cloud.
As such, an enterprise that has chosen to implement a private cloud has chosen that model because it has very specific service and compliance objectives that cannot be addressed by a public cloud.
Most private clouds are good at spinning up virtual machines in a “cup dispenser” style. You can get cups in various sizes and colors, but the focus is on standardization and automation of the basic infrastructure. However, a lot of organizations don’t realize that most private clouds aren’t necessarily architected to address key application requirements or manage critical governance, risk and compliance issues.
In most cases, many of the unique attributes needed to support an enterprise-class deployment are not available from the “standard” cloud management environment. This is due to the fact that cloud computing is still in a nascent state. For example, Amazon’s Elastic Compute Cloud (EC2) went into full production in October 2008. Back then, watching someone demo infrastructure-as-a-service was like watching a parlor trick:
Demonstrator: “Please tell the audience – have I ever met you before?” Spectator: “No, you have not.” Demonstrator: “OK – please select a service – any service – from the catalog and tell me what it is.” Spectator: “It’s a Windows 2008 server.” Demonstrator: “OK – now watch that my fingers do not leave my hand.” <Relatively short pause> Demonstrator: “… and here’s a lovely VM for the pretty lady.” <Demonstrator exits stage left to thunderous applause>
Sound familiar?
A few years later, we moved past the initial awe of the cloud and recognized its game-changing implications within the enterprise. Although “Utility Computing” sounded like a good idea, it required a service-oriented approach as well as the proof point (provided by Amazon) to appeal to the stakeholders who, up until 2008, insisted they had to hug their servers for dear life. Around 2010, savvy CIOs were “kicking the tires” by using cloud computing to automate a particularly operational-intensive part of their workloads – test, development and demonstrations.
But now, the industry is moving beyond the “kicking the tires” stage and stepping up to critical enterprise applications. We have moved beyond merely supporting test, development and demonstration workloads and into supporting production and mission-critical workloads. Just as our effort to standardize and virtualize the previous workloads was non-trivial, neither is this next step. Enterprise applications are, for the most part, sponsored by “Application Owners” within business units. In cloud terminology, we define a “tenant” in a cloud as the owner of a specific partition of the cloud that is dedicated to that owner. Each of these tenants has very specific governance, risk and compliance objectives. For example, compliance with minimal response times may be a requirement to reduce the risk of impacting the corporate image. High availability may be required to avoid the risk of non-compliance regarding the delivery of services to customers. Security compliance may be an issue for an application owner who is required to protect sensitive, regulated information. Certainly, a separate cloud could be created for each application owner, but then we would be right back where we started, with dedicated, underutilized resources, a lack of standardization, and escalating administrative expenses. Instead, a robust private cloud solution is capable of providing disparate service and compliance objectives for the various applications within a single, automated environment.
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Posted by Bill Gerneglia in Untagged
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The following is a description of current and emerging Big Data database technologies. The discussion is based on the objectives of these technologies as well as the type of data involved.
First Some Noteworthy Data Facts
An interesting implementation fact in most successful Big Data systems show that that the value of an individual piece of data decreases with time and the value of a collection of data rises with time. Additionally, the value of aggregated data should continue to increase over time, and closing the gap in the time taken to extract, transform, and load a data item will increase the value of the data more rapidly as the system tries to approach the theoretical concept of real-time decision making. Like many well engineered systems, the closer we get to zero defects and real-time processing, the more expensive the implementation becomes for the system owner.
So how do we most effectively achieve our Big Data decision making objectives given the tools available today? By selecting the proper database management tool that most closely matches our analytical decision making requirements.
In the world of database management systems used today for processing Big Data we have the following solutions:
1. RDMS/SQL - These are the traditional relational Database management systems that use the traditional relational tables and indexes that we're used to. Some examples are Microsoft SQL, Oracle, MySQL, etc.
Benefits:
A well understood and consistent model meaning an application than runs on MySQL can be altered to run on Oracle without changing its basic assumptions.
Maintain relational integrity. ACID guarantees, ie ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties that guarantee that database transactions are processed reliably.
Comprehensive OLTP/transaction support. Strong OLAP/analysis tools, often built in (MS Analysis Services, Oracle OLAP)
Problems:
Most solutions are expensive.
Scales up (i.e. bigger servers), but struggles to scale out (i.e. lots of servers). Also expensive. Not 'natural' for developers, which results in translation overhead and common mistakes like N+1 errors.
2. NoSQL - In-memory non-relational databases
These don't support the SQL language (hence the name) but more significantly don't support ACID or relationships between tables. Instead they're designed to query document data very quickly.
Examples: Hadoop, MongoDB, CouchDB, Riak, Redis, Cassandra, Neo4J, MemBase, HBase, etc
Benefits:
Cheap, mostly open source implementations. Systems can scale out very easily, tables can be readily sharded/federated across servers.
Most store native programmer objects, so no translation to tables.
Very, very fast at finding records from massive datasets.
Problems:
No common model and there is quite a bit of differences between the many solutions.
No ACID guarantees, instead high fault tolerance must be built into the application.
Transactions are at the row level only (if supported at all).
Poor at aggregation - where an RDMS solution would use SUM, AVG and GROUP BY a NoSQL solution has map-reduce, which (some minor optimizations aside) has to do the equivalent of a table-scan.
Poor at complex joins, although arguably this is something you'd design differently for.
3. NewSQL- In-memory relational databases
NewSQL is a class of modern relational database management systems that seek to provide the same scalable performance of NoSQL systems for online transaction processing (read-write) workloads while still maintaining the ACID guarantees of a traditional single-node database system.
These maintain ACID and relational integrity, but are in memory (like NoSQL) and readily scalable. They support SQL syntax. These are relatively new implementations and many traditional database vendors have rolled out their own solutions with the same capabilities. Think Oracle, Sybase, and even SAP with their in-memory HANA solution.
The most popular NewSQL systems attempt to distribute query fragments to different data nodes. These are designed to operate in a distributed cluster of shared-nothing nodes. Here nodes typically own a subset of the data. SQL Queries are split into query fragments and sent to the nodes that own the data. These databases are able to scale linearly as additional nodes are added.
Examples: Clustrix, VoltDB, GenieDB, etc.
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