August 2020 - Indium

IPL Player Prediction using Player Performance Analytics

Posted on August 31, 2020 by Abhimanyu Sundar

The IPL Fever!
Cricket is a sport that captivates audiences and fans around the world. It is played on the international stage and is a global phenomenon. Different formats of the game are in existence today and the most fast paced and most watched format is the T20 format. 3-hour games with 40 overs per game makes it exhilarating to play and watch. After the international schedule concludes, domestic competitions take place and that is what gave birth to one of the most expensive and most watched leagues in the world, the Indian Premier League (IPL) in the year 2008.

The format revolves around 8 teams who go into an all-out bidding war where they buy players in an auction prior to the start of the tournament. With the team needing to comprise of the perfect balance of batsman, bowlers, all-rounders and a wicket keeper, buying the right players is extremely important. This is where player performance analytics plays a huge role. Teams are required to purchase the right player for the right position from a large talent pool of Indian and foreign players. With the same standard auction budgets in place for every team, each and every player needs to be analyzed based on their strengths and weaknesses.

The need for player performance analytics!

Traditionally, team owners would bid for players based on a combination of the player’s reputation and the coach’s personal opinions. This led to all teams bidding exorbitant sums for a small group of famous players who were in many cases not ideally suited for the teams bidding for them. Additionally, there was no bidding consultant capable of advising on the performance or playing style of each of the hundreds of relatively unknown and overlooked but potentially talented players.

In order to help a team with a successful auction, Indium Software helped an IPL team by predicting which players to pick for which position. The foray of data analytics into sports has been rapid over the years and has paved its way into cricket as well.

The client who reached out to Indium is a technology-centric Sports Consultant who advises professional teams across different sports on strategies that lead to performance enhancement.

The requirement given by the client was rather straightforward. They wanted to tap into the pool of players who were unknown yet supremely talented. They wanted to build their team by spending optimally but getting the most talented roster in the league. Hence, the below points illustrate what they were looking for from Indium:

Recommendations on which players to bid for and the analytical reasoning via statistical evidence.
A ranking list of the most promising players by their playing position using CPIs (Composite Performance Indicators) which were to be developed in conjunction with domain knowledge.
The rankings should leverage years of highly specific player & game statistics and be objective, comprehensive (50+ criteria) and account for players’ ‘form’.
Coaches should be able to scan the rankings and infer which of the players best fit their teams’ needs by digging deep into the accompanying analytical metrics.

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Bowling over the client with our solution!

In order to achieve this, Indium had to analyze tons of data and come up with a solution that would bowl over the client. Indium implemented the following solution:

The solution pertained to two cases – Ranking bowlers and batsmen separately using different criteria for each. For both cases, the preliminary steps of data cleaning and data aggregation were performed.

Data Cleansing – The data was cleansed and formatted by combining unrelated data sets across games, tournaments and country leagues to form a unified, structured database.
Data Aggregation – In a sport like cricket where multiple data points for a playing medium like batting can be collected, the aggregate statistics for each player can be highly complex. The preliminary set of relevant aggregates were chosen after brainstorming with the client.
Index creation – To rank the list of players, the team created formulae and algorithms to evaluate player performance using analytics.
1. Compiled broad aggregate statistics for each individual player.
2. Ascertained the relevant metrics which drove good player performance for each department role (bowling/ batting) using statistics and domain research.
3. Advanced analytics techniques were leveraged to generate relevant, dependable and detailed statistics which exposed the players’ strengths and weaknesses.
Two methods were used for calculating a Composite Performance Index.
1. A Descriptive method – using formulae to derive the bowling and batsmen strength.
2. A Predictive method – using ML methods on historical data to determine the index.

Indium’s Impact on Auction Day!

The impact that this had on the team selection process was mind boggling. Indium’s solution gave the team a huge competitive advantage. The results from Indium’s solution are as below:

Most of the top 10 most bid bowlers and batsmen figured in our recommendations.
The recommendations narrowed the pool of players from 350 to 20 permitting the coach to target his focus.
An objective and comprehensive ranking of each available player (indicating performance) was presented alongside revealing statistics (indicating team fit).
The team was able to plan its bidding strategy which led to it utilizing only 70% of its bidding budget.
Indium discovered high performing and good-fit players who were not on the team captain, coach or team owner’s radar.
Indium provided precise statistics of the selected players’ strengths and weaknesses to leverage during team training.

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This led to the IPL team being very successful in the auction and having a stunning roster. This further allowed the unknown players to come into the spotlight due to their performances. As always, we were delighted to see a happy client and our work spoke for itself during the auction. Are you looking to derive actionable insights through performance analytics to improve team performance? Reach out to us, we would be glad to work with you.

How To Store Social Media Data For Analytics

Posted on August 28, 2020 by Suhith Kumar

More than 2.5 quintillion bytes (that is 2,500,000,000,000,000,000) of data is created every day, says DOMO’s Data never sleeps report from 2018. Data is growing rapidly, and social media is contributing to the surge. An estimation is that, in 2020, 1.7 megabytes of data is created every second for every person on the planet.

These numbers are from internet users searching for information on a search engine, signing up to a social media network, posting a tweet, comment or a status update, a photo in a relevant channel, watching a video, downloading a song, and so much more, resulting in the proliferation of data.

Data has and will continue to be a key asset for businesses for it helps observe market trends, understand customer pain points, improve customer experience, enhance a product feature, et cetera.

Social media data is a gold mine of information. It shows how your target audience engage with your content, the type of content they engage with the most and more. It is possible that most of your social media followers will offer opinions, share their sentiments, provide product feedback and ask for recommendations.

Every reaction or engagement (like, retweet, share, comment) is a piece data that, if mined, provides valuable insights about your brand and your products, and reveals market trends and customer behavior.

Storing social data

Before businesses analyze data to make key business decisions, it needs to be collected and stored in a way that’s easier to manage and access. It’s also essential that the data repositories are protected against cyber threats to ensure confidential and key data isn’t stolen or damaged.

Social management applications

Social networks do not sleep, and data is generated round the clock, which is why a social management tool is key to monitoring the conversations about your brand.

Handling social media data also requires storage solutions that can provide information in real-time, which is achieved with the help of social media tools and applications.

They store all your brand mentions on social networks, enabling you to group conversations and profile mentions with special filters to identify those most important to your brand.

Being able to manage large volumes of social data is another key facet of social tools.

Data warehouse

Storing your social media data in separate tools, in other words siloing your data, is detrimental to deriving key information for your social marketing strategy.

All your tools collect and store data separately but if you were to change services at any point, you might lose all the data stored in the tool.

Centralization and ownership of data sets not only overcome the limits of data silos, but they are effective for analytics. Once all your data is centralized, in a data warehouse, the plethora of Business Intelligence (BI) tools can help glean actionable insights.

Data archiving

Data is almost always delivered in real-time, with social media data being a prime example.

Archiving your social media data is essential to performing analytics to gain customer insights. To get the most out of your social analytics platform, you must group the small objects (such as tweets) into a large file for analysis.

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Ensure that you capture the context of all your archival content to make it complete. In addition, your social archival data must be searchable and navigable so that you find anything you’re looking for, from data such as a user liking your tweet and retweeting it to a conversation about your brand.

Graph database (GraphDB)

It is a data management solution that handles large sets of structured, semi-structured and unstructured data. It enables businesses to access, store and analyze data coming from different sources and is useful for integrating social media data to perform analytics.

According to an IBM survey, 57 percent of brands across industries who used GraphDB reported improved performance and speed in managing and analyzing data.

GraphDB has the capabilities to store, analyze and retrieve high-velocity data, which applies to social media networks.

The technology provides brands with a broader, deeper visibility into their data as they try to understand correlations and derive key insights.

Summary

Social media data—which at a basic level comprises metrics such as impressions, shares, retweets, comments, and on a more advanced level includes conversion rate, referrals and enquiries—is not just vital to your marketing strategy across channels but it reveals key information about your brand, your products and the sentiment shared by your customers and prospects.

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By collecting data and performing social data analytics, brands make informed decisions that boost their image online, help gain a competitive advantage, understand customer behavior, among other benefits. But initially, it’s essential to have a data management strategy which allows data (irrespective of volume, velocity and variety) to be stored and analyzed to glean actionable insights.

Docker for Software Testing

Posted on August 28, 2020 by Bali Reddy

Gone are the days where we had different physical systems for developing & testing applications and along with the setup of dependent software. The development of virtualizing hardware inside the system broke away those physical limitations.

It’s a tiring process for a QA team, to build and maintain the test infrastructure that is needed for Test Automation. Though we have cloud services like browser stack, sauce Labs who provide the needed virtualization, comes with their own limitations like cost, performance and security challenges.

On the other hand, Docker is a light weight platform which allows to pack your app in a container with all its required dependencies thereby setting up the Test Automation infrastructure easily, especially with open source Test automation tools like Selenium and Appium (along with their respective packages / servers), where there is no cost involved.

Quick information on Selenium, Appium and Docker Containers:

Selenium is an open source UI Test automation framework for web and web-based applications. It simulates user actions on different Web browsers and validates the functional flow of the Web application. It uses “Selenium-Grid” concept to run the test scripts in parallel distributed infrastructure to pace up the Test Automation execution speed.

Appium is an open source mobile automation tool. It supports Native, Web and Hybrid applications across platforms (Android and iOS). It uses Client-Server architecture to create a communication channel that translates the selenium scripts to device understandable commands.

A Docker image is an opensource tool that provides platform as a service. It uses virtualization at OS-level to deploy software in independent packages. These packages can be read, modified and executed with help of Containers. The advantage of containers, these are isolated from one another and bundle their own software, libraries and configuration files.

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Now let’s understand on how to leverage Docker Container for a test runner, a selenium Grid, and an appium server, to construct a flexible and disposable test automation infrastructure.

Selenium appium architecture using docker

Test Runner container:

Test Runner is a typical illustration of a test automation tool/ framework. In addition to test automation framework, one should consider dependent libraries and their version, platform/ environment utilities and their access. These automation solutions and their dependencies can be effectively dockerized (bundled) into a docker image.

Selenium-Grid container:

Selenium-Grid container has Selenium Hub and node servers. It allows to run test scripts in parallel and in distributed fashion where different tests can run at the same time on different machines to save execution duration. Selenium Hub is center for managing which machine your Selenium test will run on. To run a Selenium test, we have to configure the machine and browser related information. Based on the configured details the test will execute on the desired machine and browser combination.

We have to install Selenium servers over multiple machines which is a tedious job. To make this process easy, selenium provides a docker image for Selenium servers. By running Selenium servers (hub and node) in containers, it is very easy to setup and configure the Hub server and to scale up the number of node server containers.

Even more, there are a few open source projects that provide extra functions for Selenium-Grid by extending the Selenium Docker image. Zalenium is one open source project powered by Zalando. It is highly flexible and auto scalable. It has ability to spin up selenium docker containers instantly as nodes without any manual intervention.

The main objective of Zalenium is to have a disposable and flexible Selenium Infrastructure for everyone and in an automated fashion. It also has video recording feature that can be viewed using a VNC player which is live preview board.

Zalenium provides docker-selenium nodes that are created on-demand and disposes of itself after test execution without providing any commands. With this, the test cases can run very fast.

Appium container:

Let’s have a look at the software required for mobile automation setup with Appium.

Appium
Supported programming language and it’s runners
Build tools like Node
Mobile device’s dependent libraries
Android and Java environments
Testing framework
Android Emulators or real devices
iOS Simulators or real devices

Appium framework may not run as per the requirement even if there is any problem with one of the software. This will be definitely a difficult job to configure, scale, maintain and dispose.

Will bundling all these software in a single container make my work easy?

Yes, in docker Hub, there are some images such as appium which contains all of appium’s dependencies in the docker form.
The main goal is to help the user focus on writing the UI tests by leveraging the advantages of using a docker like

Having readymade test infrastructure to deploy on demand.
To switch between cloud platforms easily
No need to expertise on how to install and configure the dependency tools
Tester can primarily focus on writing tests and achieve efficiency and effectiveness in results

In Summation:

Docker can be leveraged for automation testing in addition to providing packaging and deployment support for software quality assurance services. This helps in setting up and scale out remote servers either for web UI or mobile testing, easily. By having an isolated and stable environment, where everyone can perform testing inside a container to verify the system functions at any development stage.

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All containers in this infrastructure can be created on demand and destroyed when the job is done. It makes the test infrastructure more flexible and maximizes the availability of machines and devices.

Serverless architecture for COVID-19 time series data by John Hopkins University — AWS

Posted on August 21, 2020 by Srinivas paturu

In this article, I would like to share the code for a simple and effective transformation to tackle the time series format data file that is published by John Hopkins university.

I would be eventually trying to port all the scripts to cloud on AWS and setup up a truly server less architecture.

The time series data file format is as below with the header:

This format is repeated for confirmed, death and recovered count of cases for global and US geographies.

The data recorded for the next day is appended to the file in a new column. Naturally, this horizontal format of laying the time series data is not conducive for analysis in most of the business intelligence tools. The data is more efficient if the column of dates in the above file are transformed to rows.

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The operation is just a breeze in python using Pandas data frames and pivot functions. Here is the code

The transformation steps are in the following lines from above:

This line would pivot the dates from columns to rows.

These couple of lines are a smart way to use the diff() function with group by to get the equivalent of LAG() operation in a window analytical function in SQL.

The next part of the this article is about integrating this script with the AWS Lambda function to truly go Serverless.

For this to work, I borrowed the knowledge from another awesome Medium article on how to enable Pandas library on AWS Lambda. Without pandas there would a very tedious process of operating on numpy arrays instead of data frames.

The plan is succeeded with the following steps:

a. Created an AWS Lambda function with the following code (for the recovered cases file, which you can replicate for other files)

b. Created an Event rule on AWS CloudWatch that would trigger the above Lambda function to execute on a schedule of every X minutes.

c. The Lambda function would write the transformed file to S3 bucket.

Architecture Note

I have chosen the route of AWS Lambda to AWS S3 to stay within the limits of AWS free tier services. AWS Lambda are primarily intended for fast executing micro services and they are not supposed to do heavy data lifting, however we can use it for doing data lifting. The most optimal choice for heavy data transformations would be to execute an AWS Glue job on PySpark, but that would be outside of free tier limits.

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The last part of the guide is to enable AWS Quick Sight to generate a quick visualization layer for the data.

From the quick sight console, click on the new dataset option to import the S3 output files to SPICE (the high dimensional cube for business intelligence in AWS). Then add other data sets and join on the key (Date, Country/Region and Province/State). The final dataset would look like this below:

Then you can click on Save & Visualize option to start preparing your dashboards. Here are few I have made:

Hopefully this would help your journey in creating the COVID-19 dashboard.

Selenium 4.0- The Latest Test Automation Tool

Posted on August 21, 2020 by Vaibhavi Tamizhkumaran

Selenium being one of the leading test automation tools in the industry, serves the purpose of test automation at its best. The first ever Selenium tool was launched in the year 2004 as Selenium Core. The Selenium test automation service has a few additions in the year 2007- Selenium IDE & Selenium WebDriver.

The next generation Selenium tools were named Selenium2 (2011), Selenium 3 (2016) and after a three-year gap, Selenium was to launch its latest version- Selenium 4.0. The release had been delayed and a trial version of selenium- Selenium 4.0 alpha was released. Let`s look at the new additions and modifications that have been made.

What`s New?

SELENIUM IDE: The Selenium IDE supports a rapid test development process and it does not need an extensive programming knowledge.

SELENIUM WEBDRIVER: The Selenium WebDriver is a user friendly and has a flexible API available in most of the popular programming languages and bowsers.

SELENIUM GRID: The Selenium Grid is another new upgrade that allows to distribute and run tests across multiple machines/ systems.

Upgraded Features In Detail

Selenium being the talk of automation testing industry has released the Selenium 4 Alpha that is to be upgraded into the Selenium 4.0. The following features can be spotted in the Selenium 4 version;

1. Web Driver Changed To W3c (World Wide Web Consortium) Standardization:

The Selenium 4 has a change in standardization to W3C to encourage the compatibility across various software implementations of the WebDriver API. This change ensures that the communication does not require the encoding and decoding of API. This results in a more stable framework and reduces compatibility issues across various web browsers.

2. Improved Selenium Grid

The Selenium Grid has been improvised in terms of its UI and stability. The coding of the Selenium grid has been changed completely and the console of the grid has been restructured. This allows the execution of the test cases in parallel on multiple browsers and operating systems. Now, the Grid can serve the purpose of the Node and Hub.

The UI grid of Selenium 4 has been created to be more user friendly and has all the relevant information regarding the session’s capacity, run time and other such details. Another addition to the grid is the support for utilizing the Docker containers along with the grid server.

3. Friendly/ Relative Locators Introduced

Selenium provides multiple explicit locators such as id, XPath, etc… the new locator provides a way to locate the element by its position by concerning other elements such as above, below, to-left-of, to-right-of & near.

4. Support For Browsers

The existing support for Opera & PhantomJS is to be removed. Users who wish to test Opera can go for Chrome and users who wish to test PhanthomJS, can go for Chrome or Firefox in the headless mode. The HTML unit is no more default on Selenium server.

5. Selenium IDE (Chrome & Firefox):

The Selenium IDE is a tool for recording and playback options which is available with many more advanced features and capabilities.

6. New Plug-in

The old version of Selenium IDE could be run only on Google Chrome, but the latest version of Selenium 4`s plug-in allows the user to run Selenium on any browser (Firefox, Google Chrome, Internet Explorer, etc…) which can declare the vendor location strategy.

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7. New CLI Runner

The latest version of the new CLI runner is a WebDriver that is based on Node.JS codes. This gives the capabilities of Playback and parallel execution for supporting parallel execution and further helps in providing reports (test reports- pass & fail).

8. Detailed Documentation

The users of Selenium face many difficulties such as late updation of documentation. The new release promises to deliver updated documentation.

9. Better Analysis

There has been enhancements in terms of analysis- Logging & debugging details have been improvised to fasten the resolution of the script issues for the testers.

10. Network & Performance Analysers

In terms of network analysers, the capabilities such as Intercepting requests, Emulating network conditions by changing the type of connection, enabling the network tracking has been revised.

In terms of performance analysers, there has been updates on the support for chromium-based edge browser, full page screenshot on Firefox & element level screenshots. Also, performance package analysers give the provision to analyse the runtime performance by providing some methods for collecting and reporting the duration metrics.

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With the introduction of many new test automation tools and techniques in the automation testing industry, the Selenium test automation tool is always an edge over them due to its combined potential to attend to many testing needs of organisations.

Selenium 4.0 gives the user the best experience and capability to do all the unfulfilled tasks by its previous versions. It is faster, more compatible making it the most efficient automation tool in market.

Internet of Things (Iot): Challenges For Businesses In Adopting The Technology

Posted on August 21, 2020 by Suhith Kumar

Smart home appliances, smart security systems, fitness trackers, wireless headphones/earbuds and so many more have become an intrinsic part of our daily lives that if we were deprived of them, we’d feel a void somewhere.

Amid the COVID-19 threat, drones, too, have become vital to our sustenance. They have been used for delivering medicines and other essential goods to hospitals and healthcare centers, disinfecting coronavirus-affected areas, and for surveillance to enforce social distancing.

Those devices fall under the Internet of Things (IoT) umbrella, which, make no mistake, is now one of the mature, increasingly mainstream technologies along with Artificial Intelligence, Machine Learning, Automation and more.

According to data from Juniper Research, the number of IoT devices would have shot up to 38.5 billion by the end of 2020, an increase by 285 percent since 2015.

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The proliferation of IoT, however, has been fairly recent. It reached the commercial market in 2014, fifteen years after the term was coined by a British Technology expert, Kevin Ashton, initially to promote radio-frequency identification (RFID).

IoT’s growth rate will continue to rise and permeate more sections of the society. According to a Kaspersky report from April 2020, 71 percent of the organizations in the IT and telecom industry already use IoT, while 68 percent of companies in the finance industry use the technology. And it’s only a matter of when, and not if, more businesses adopt the technology to streamline their operations, reduce costs, increase efficiency and discover new revenue streams.

Implementing IoT, however, does present a few challenges for businesses.

Technical expertise

One key facet of successful IoT adoption is having the technical expertise to configure the devices for maximum performance but also to ensure they don’t have, for instance, a security hole which could be exploited.

According to a Gartner report, IoT integration is cited as the primary hurdle, with 50 percent of the enterprises reporting to not having dedicated teams, processes or policies to implement and maximize the technology.

Poor configuration of the devices may disrupt business operations and ultimately make the upgrade futile.

On a fundamental level, IoT deployment comprises two parts. The first is the physical aspect of handling the connected device and its operation. The second is the cyber element.

Earl Perkins, Gartner’s managing vice-president, says: “Knowing when and how you must secure the physical element is going to be a major focus for many data-centric IT organizations, and usually requires engineers to assist.”

Security

Due to their connectivity and access to business networks, IoT systems are particularly vulnerable to cyberattacks. With multiple devices being connected to the internet, each becomes an entry point for attackers to gain access to a network and expose confidential data.

How can companies ensure security when handling IoT devices?

Some standard best practises apply:

Create a secure password and update it regularly (Note: IoT devices may have default passwords set by vendors for initial configuration and can be difficult to change or cannot be changed)
Protect routers with a strong password, and set up a firewall to monitor traffic between the internet connection and IoT device
Ensure the IoT devices are updated with the latest patches and security updates
Consider connecting IoT devices to a network that doesn’t connect to computers storing key data
Check if all the devices need to be connected to the internet

Connectivity and workload

Network and bandwidth constraints are being felt with the abundance of internet-connected devices.

Most IoT features need lower latency for effective performance and may require local servers or service providers to provide fresh bandwidth and QoS to manage workloads with unique requirements. Implementing this may not be cost-effective, let alone the human cost of operations.

To overcome this, companies may look to increase the processing capacity of the devices and improve connectivity performance by leveraging edge data centers to handle some of the computing workloads. Being close to the network edge, they will help reduce latency and process information faster.

Another advantage of edge data centers is they can resolve connectivity issues by extending network services into remote areas.

Data Protection and Privacy

All interactive electronic devices gather and store user information, which may include their diet plan, their work location and a whole lot more.

It is no secret that IoT devices gather accurate data from the physical world. While that’s desirable for organizations from the analytics viewpoint, a user might not be convinced with sharing the data (even if it doesn’t contain personal information) externally.

According to open-source web application security project, the major privacy risks include web application liabilities, data leakage on the operator side, sharing data with third parties among others.

Though it goes without saying, the purpose of collecting data, expiry and security must be clearly stated in the information security policy, while organizations must carry out a risk assessment of the consequences associated with processing.

Support

The capacity to detect, analyze and resolve issues pertaining to IoT devices is integral for successful adoption of the technology.

This is a major challenge for organizations considering the shortage of relevant skills and the overwhelming number of connected devices that may require service and support from the IT department.

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Original equipment manufacturer warranties, while expensive, can help companies with continuous monitoring and analysis and lessen maintenance costs. It also allows organizations to utilize their resources more effectively.

Summary

IoT enables organizations to innovate and grow by providing data-driven insights into the productivity and performance of their processes and systems, by providing new ways to understand customer behaviour and pain points, and by creating new business opportunities.

Therefore, companies have plenty to gain from implementing IoT, which does pose challenges before, during and after deployment. However, with a combination of technical expertise, support framework and cybersecurity protocols, organizations can leverage the transformative capacity of the technology.

QA for an Online Streaming Services Application – A Success Story

Posted on August 5, 2020 by Pradeep Parthiban

About online streaming services application

In the recent past, there’s been a considerable increase in online streaming services over traditional media. Cutting the cable is the popular trend with more people preferring online streaming options.

According to Statista, the online streaming services market is projected to reach USD 85,735 million in 2025 from USD 51,617 million in 2020 at an annual growth rate of 10.7% (CAGR 2020 -2025).

The main reason behind this rapid growth is the growing adoption of cloud-based solutions. And by 2025, there will be 1,337.1 million estimated users.

It’s anticipated that adoption of online streaming services will reach its peak, particularly in developed countries.

One could say that the COVID-19 pandemic has accelerated this increased adoption. According to data, there’s been a 10% increase in viewership during the lockdown.

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This isn’t a big surprise with 1/4^th of the world population in some sort of lockdown due to corona virus, thus encouraging people to sign up for video streaming services.

Software testing plays a vital role in online streaming applications. Apart from tough competition, there are other challenges these online streaming services face such as multi-platform presence and staying updated on technology.

People nowadays want unlimited accessibility and control over video streaming services.

The ability to stream your content on a variety of platforms has also been crucial in driving the growth of these streaming services, which need to be compatible with the latest technology.

The users always want the best experience, hence there is a need to constantly update the UI, library reclassifications and security.

To keep up with the market, organizations must consider testing their application, through software testing outsourcing for instance, for bugs before the application becomes accessible to the consumers.

Client overview

In the world of online streaming services, it is the audience who decide when and what to watch and if your video takes more than a few seconds to start, you might just lose another member of your audience to your competition.

Every additional second of delay will result in more of your subscribers leaving your services and moving to your competitors.

Our client is an online video streaming channel offering subscribers content of 2000 titles at any given time.

The company is a premium cable channel (can be attached to Amazon Prime) with four live channels, selection of movies and original shows.

They had an application for streaming which is a typical media application with features such as sign up, watch movies, favourites, downloads and collections.

The application can also be accessed through multi-platforms such as Xbox One, Chromecast, Apple iPads, iPhones and Android devices and smart TVs.

Business challenges

Seamless delivery and playback quality are two of the most important challenges faced by an organization in this field.

As a leading Quality Assurance (QA) service provider, we have to ensure the application is of a good quality. Our client came to us with the following business challenges:

The functional reliability of the application
Seamless UI and UX of the platform/recommend features based on competitor research
Consistent multi-platform experience across most popular market devices
Deliver a high-quality mobile application with a launch ‘right in the first time’

Our solution

At Indium, we have a dedicated team of experts in testing the online streaming application. As a first step, we carefully analyzed the workflow of the application and designed module-based Test Cases for both positive and negative scenarios

Test automation for a faster delivery cycle and exploratory testing to identify showstoppers and initiate early/parallel defect fixing were our suggestions.

We also kept definite metrics to tick off user experience (UX) indicators such as app launch experience, network, interruptions, display, list of conventions in a typical media/entertainment app etc.

Our exclusive team tested the application for functionality/UI/UX/Compatibility across 150 mobile devices. We identified various bugs and sorted them based on their criticality, recommending priority of action.

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Conclusion

The online streaming industry is evolving in terms of technology and to the surprise of many it is growing rapidly. Proper QA is necessary for customer acquisition and retention.

Indium software has over two decades of experience in quality assurance and has worked with some of the top logos from around the world. Our team helped meet our client’s business requirements by enhancing the application and ensuring a consistent experience across platforms.