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)
