by admin | Jan 12, 2018 | Software Testing, Blog |
While more and more businesses are accepting Cloud Computing services, most businesses are not clear of what Cloud Computing is, how it associates to their specific business sector and more importantly how it can cater their business needs. This post maps out this missing information about Cloud Computing for the business of every size.
What is Cloud Computing?
Cloud Computing refers to IT systems and resources that can be accessed through the internet rather than equipment that a business has to buy setup and manage on their own. Cloud Computing permits an enterprise to use IT as a utility, provided as a service by a service provider and you pay for what you use. Cloud Computing is often called “Hosted IT Solutions” or “Software as a Service”.
Cloud computing has three cloud computing models.
What is Infrastructure-as-a-Service?
Infrastructure-as-a-Service (IaaS) is the building blocks of computing that can be rented: physical or virtual servers, networking, and storage. This is alluring to organizations that want to develop applications from the very ground up and want to manage all the elements on their own, but it does need firms to have the technical skills in order to align services at that level.
According to the research of Oracle, “Two-thirds of IaaS users confirmed utilizing online infrastructure makes easier to organize, had cut their time to develop new applications and services and had lowered maintenance costs. But half said IaaS is not wholly secure for most sensitive data.”
What is Platform-as-a-Service?
PaaS is the next layer up. This includes the tools and software that developers want to build applications on top of that could encompass middleware, database management, operating systems, and development tools.
What is Software-as-a-Service?
Software-as-a-Service (SaaS) is the version of cloud computing and is delivery of applications-as-a-service that most people are used to. The underlying operating system and hardware are irrelevant to the end user, who will access the service through a web browser or app; it is usually purchased on a per-seat or per-user basis.
According to researchers, IDC SaaS is the dominant cloud computing model in the medium term with more than two-thirds of public cloud spending in 2017, which is likely to drop a bit to just under 60 percent by 2021.
The significant SaaS spending is made up of system infrastructure software and applications, and IDC said that spending is likely to dominate by applications purchases, which will lead to more than half of all public cloud spending by 2019. By 2021 customer relationship management (CRM) applications and enterprise resource management (ERM) applications will be responsible for about 60 percent of all cloud applications spending.
Examples of cloud computing
Cloud computing provides a wide range of services. It involves consumer services, such as Gmail or the cloud back-up of the photos on your smartphone, allowing organizations to host their data and operate their applications in the cloud. For instance, Netflix depends on cloud computing services for its video streaming service and its other business systems too, and have various other organizations.
Cloud computing is a default option for different apps. Now, software vendors offer their applications as services on the internet rather than independent products as they switch to a subscription model. But there is a downside to cloud computing; it can also drive new costs and new risks for businesses using it. If you’re planning to migrate to cloud, you can take training like AWS training to understand more.
Why is it called cloud computing?
An important concept behind cloud computing is the site of the service, and the details of the operating system or hardware on which it is operating, are mostly irrelevant to the user. This is because the metaphor of the cloud was taken from old telecoms network schematics, in which the cloud was represented in the form of the public telephone network to show that the underlying technologies were not appropriate.
What are cloud computing services available?
Cloud computing services offer various options from the basics of networking, storage, and processing power through to natural language processing and standard office applications along with artificial intelligence. This means any service that does not need you to be physically present with computer hardware that you are utilizing can now be provided through the cloud.
Hype of Cloud Computing
Developing the infrastructure to back up cloud computing now accounts for about a third of all IT spending globally, as per the research from IDC. Simultaneously spending on in-house, traditional IT continues to fall as computing workloads continue to shift to the cloud, whether that is public cloud services provided by vendors or private clouds developed by enterprises on their own.
According to 451 Research, “About one-third of business IT spending will be on cloud services in 2017, indicating a higher reliance on external sources of application, infrastructure, application, security services, and management.” Moreover, analyst Gartner says that half of the worldwide enterprises utilizing the cloud will have gone all-in on it by 2021.
Furthermore, spending on cloud services is likely to extend $260bn this year up from $219.6bn. It is growing beyond the expectations of analysts. However, it is not completely clear how much of that demand is evolving from businesses that really want to shift to the cloud and how much is developed by vendors who only provide cloud versions of their products (usually because they want to move away from selling one-off licences to selling more profitable and predictable cloud subscriptions).
by admin | Oct 10, 2017 | Software Testing, Blog |
The most important thing to understand about data quality is that the senior management needs to lead it for it to be implemented widely. It is up to the middle managers to get it started through their own individual initiative. It is possible for most of them to be able to bring on the needed changes as long as it is within their own chains of command. But since middle managers do not have the kind of influence needed for the data quality program to penetrate into the entire system of the organization, they need help from elsewhere.
There is no doubt a lot of factors at play when it comes to a great data quality program in an organization but what is most important is that the breadth and seniority of the managers who lead the effort are brought into play. The more senior and the broader the manager is, the better his influence plays on the program’s reach.
An organization is going to do very well with data quality if it can cause improvement in the management of its data. Data quality forces people to act and think differently. When the leadership is powerful, senior and broad, the chances of failure decreases on its own.
A great way of providing necessary leadership is by organizing a data council. If that is not possible, one can also go with an already existing council, such as the operations department of an organization. The best way to go about it would be to put the chief executive as head of the data council or the equivalent.
Councils have the following responsibilities:
- Leading the data quality effort
- Deploying management responsibilities for data
- Supporting information chain and supplier management
- Managing the “data culture”
- Ensuring that data quality efforts are adequately funded
Roles and Responsibilities of Senior Management
Lead the data quality program
1) Formulate business case
- Most important issues/opportunities relevant to data (i.e., cost reduction, customer satisfaction, competitive advantage, etc.)
- Expected returns
2) Formulate and promulgate quality policy
- Role of data quality to organization’s strategy
- Managerial responsibilities
- Targets for continuous improvement
- Contribution to merit rating
3) Select major dimensions of data quality
- In customers’ eyes (accuracy, timeliness, relevancy, etc.)
- With respect to the competition
- Cost of poor data quality (i.e., error detection and correction)
4) Communication of all of the above to important stakeholders, including key customers, employees, etc.
Support information chain and supplier management
1) Identify the most important processes and suppliers
2) Invest information chain and supplier managers with the needed authority
3) Establish the “project system” including machinery for
- Soliciting project nominations
- Selecting projects
- Charting project teams
- Selecting the team (leaders, members, facilitators, etc.)
- Supporting project team
- Reviewing results
- Celebrating success
- Ensuring the improvements are sustained
Advancing the data culture
1) Advancing the concept of data “as business assets”
2) Leading change management improvement
3) Motivating continuous improvement
4) Resolving issues as they occur
5) Ensuring that the training program is in place
- Data curriculum
- Process and supplier management
- Planning, control, and improvement processes
- Problem solving, team building, group dynamics
- Style of training
Ensuring adequate funding
1) For training
2) For data quality staff
In some respects, it may be even more difficult to lead a quality program for data. After all, data are intangible, and bad data seem to strike like viruses. Further, it is so easy to confuse data or information with the supporting technology.
by admin | Jan 6, 2017 | Software Testing, Blog |
“Enterprise data will grow 650% in the next five years. Also, through 2015, 85% of Fortune 500 organizations will be unable to exploit Big Data for competitive advantage.” – Gartner (mid-2015)
With growth and complexity of the data, comes challenges in putting the data to use and perform effective decision making. The ‘data’ that we are talking about here could be anything from a simple 3-5 word of tweet (in KB) to Photos /videos uploaded on social sites (in MB), a full-length movie on YouTube or other sites (in GB).
Now, think beyond and we have Terabyte (2 years of nonstop listening to MP3 files forms approx. 1 TB) or Petabyte (think about 100 years of television forms a PB, the total photos in Flickr site by 2011 formed 60 PB).
Challenges in data maintenance –
Ineffective decision making if the data is incorrect
Performance issues due to high data magnitude
Increased cost of handling huge volume of data sets
Heterogeneous and unstructured data leads to increased effort in reporting
Challenges in testing big data –
Huge and Heterogeneous data –
Data has grown exponentially last few years, and it will continue to grow. If you recollect a few years ago, processing few millions of records was considered a herculean task which may ultimately take a toll on the system performance thereby requiring to invest heavily in hardware and on-going maintenance.
Gone are the days of Gigabytes (1 GB = 1024 MB). Big data landscape has already seen Terabytes (1 TB = 1024 GB) and even Petabytes (1 PB = 1024 TB).
Such huge volume of data should be audited for its fitment for business purpose. Preparing test cases in this scenario has always been a challenge.
Technical expertise –
Big data is relatively a new term. Technology is growing more frequently than ever. Testing big data, unlike other aspects, need testers who thoroughly understand the big data ecosystem, have the ability to think beyond automated testing. With an unexpected and complex structure, big data can cause automated scripts to fail.
With the shortage of expertise, organizations may need to invest in training and develop automated solutions for big data. Moreover, it requires a mindset shift for testing units within an organization where testers will now have to be on par with developers in leveraging big data technologies.
Understanding data and foreseeing effort –
Without a proper knowledge of the available data, it difficult to strategize testing and derive effort requirement. It’s also necessary for a tester to understand the statistical co-relation between data and business benefits.
Example (1). Let’s consider we need to generate a report from Twitter on a topic that will capture the Emotions of people on percentage. Sounds weird? Yes, understanding the ‘emotion’ factor from the data available is the challenge.
Example (2). We have websites that help us search for similar sounding songs. Just imagine – the song metadata need to be compared with millions of songs from the database and the results need to be displayed in seconds.
Big data is more than just size. Its significance lies in 4 V’s – Volume (magnitude), Velocity ( the distributedrate at which data is generated /transported), Variety (type of data) & Veracity (accuracy and quality).
Need for Special test environments due to large data size (HDFDistributed file system)
Test automation –
Too many data, too many scenarios to be covered, too little time for regression test, many real-time services involved.
Live integration testing –
There has been a sudden demand for capturing live data and analyze in real time. Example – Weather warning systems and forecast mechanism. Data may come from multiple feeds (sources), so the data quality is expected to be reliable and clean.
Scalability testing –
We have been experiencing and discussing the growth of data in exponential terms. Applications working on big data are expected to be scalable so as to handle this increasing volume.
Performance testing –
Big data applications work with live data for real-time analytics and reporting. Performance testing is coupled with live integration and Scalability testing.
So, what are the key aspects that we need to focus on while dealing with big data testing?
Validation of structured and unstructured data
Dealing with non-relational databases
Optimal test environment
Performing non-functional testing
Tools to test big data:
Testing Whiz –
Probably a very popular testing tool, it offers automated big data testing solution to verify structured and unstructured data sets, schema, approaches and inherent processes residing at different sources in your application in languages such as ‘Hive’, ‘Map-reduce’ ‘Sqoop’ and ‘Pig’. The major features provided include – Post ETL data validation, Data migration validation and Big data health check.
Query surge –
A collaborative data testing solution that finds bad data in Big data and provides a holistic view of the data’s health. Helps you ensure the source and target data are compatible.
Contact us to learn how we can assist you with your Software Testing needs.
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by admin | Jan 1, 2017 | Software Testing, Blog |
Before we directly get into discussing testing eCommerce sites, it will be worthwhile looking at some statistics.
Cross-border shoppers have increased drastically. This eventually leads to increase in payment transactions.
The eCommerce market in India expected to cross 2 Lakh crores in 2016, according to IAMAI (The Internet and mobile association of India) and IMRB.
The Forrester research projections say that U.S eCommerce sales will reach $ 500 billion by 2018.
Ref: https://www.internetretailer.com/trends/sales/us-e-commerce-sales-2013-2017/
E-commerce Share of Total U.S. Retail Sales 2011-2015: The e-commerce share of total retail sales increased more than 4 percentage points in 5 years.
Source: U.S. Department of Commerce, Internet Retailer
Comparing year-over-year growth in total retail, non-store sales and e-commerce.
Source: U.S. Department of Commerce, Internet Retailer
We are seeing the retail landscape changing more frequently, we all are experiencing a shift from a physical to digital retail. Amidst all these, we have also seen high-profile system /technical glitches on eCommerce sites that crippled its sales and to some extent, the trust and goodwill.
Probably the one thing that differentiates eCommerce sites/eTailers/Retailers etc. is – User Experience. Thorough, well-planned, systematic, regular, detailed Testing is one of the first savior for eCommerce sites to sustain in the ever changing market.
Marketing practices have started to shift from being device-focused to people-focused. We are seeing that ‘Instant delivery’ or same-day delivery services are common nowadays.
Out of the numerous parameters that drive User Experience, some of these include – Competition, User-friendly navigation, secured shopping, Scalability, Reliability, and Performance.
Testing eCommerce software has already gained exponential momentum with the advanced smartphones, innovative apps and new gadgets.
‘Changes’ to the retail system happen every week, daily or even hourly. There always are new offers, new products, deals, promotions, etc. With changes becoming so overwhelming, Testing becomes a must to ensure customer loyalty and market sustainability.
The latest and most spoken about the approach in the retail industry is the Omni-Channel. It provides the customer with a seamless shopping experience irrespective of the customer shopping online, using a mobile device, talking to the customer care on the phone or in a brick and mortar store.
Retailers must ensure a unified customer experience across devices and channels.
We outline some guidelines for testing eCommerce software –
Creating an account, login
Registered and Guest checkout
Persistent shopping cart
Product details, display, reviews
The Search form, Sort, Filter, Pagination
Recommended products – based on search history, wishlist, etc.
Integrated Payment gateways, Secured shopping
Order modification, Cancelling order, Returning a product
Social integration
Cash on delivery
Customer support page
Customer notifications
SEO (Search engine optimization) – XML sitemap, internal indexing, Meta descriptions, etc.
Cookie auditing
Web standards – HTML & CSS validation checks
Accessibility test – WCAG guidelines, access with screen reader, access with/without mouse
Performance test
Mobile device compatibility test – Smartphone /Tablet, Apple iPhone /iPad, Android, Blackberry, Windows OS
Content detail and validation
Globalization testing
Cross sell and Up sell testing
Browser compatibility test – IE, Chrome, Firefox, Safari, UC (per requirement)
