How Testing can save Financial Applications from the next HACK

How Testing can save Financial Applications from the next HACK

On October 20, 2016, The Economic Times (Indian business daily) reported that ‘3.2 million debit cards may have been compromised in India’ as a result of a security breach. The breach allegedly originated in Hitachi Payment Services that enabled fraudsters to steal information and consequently funds. Startling incidents like these further establish the need for testing in the Financial/Banking sector.

Experts have also identified malware that can particularly infect the point-of-sale (PoS) machines, promising higher rate of success to the hackers. Considering the fact that more number of people tend to shop than go to the ATM. A similar retail breach was reported in 2013 in the US at the retail chain Target.

In 2013, data from up to 40 million credit and debit cards of shoppers at Target got stolen by hackers. This happened at the peak of the holiday shopping season. Financial Analysts and market research firms have used this data to decipher the levels at which the security of financial applications is compromised.

This further emphasizes the urgency to build a comprehensive Testing strategy for the financial/banking sector. Moreover, it is important to evaluate the overall strategy by considering the current day challenges and probing malware.

What does a financial software facilitate and why is it complex?

Financial software/applications are complex and are built on the lines of financial information management. The software can be executed as an independent software or can be embedded into a financial information system (IS). Generally, a financial software incorporates all aspects of personal or business finance to offer multiple features – basic financial data management, transactions, budget management, personal/corporate account management, and assets management.

Additionally, a Financial App offers Multi-tier functionality to power numerous concurrent user sessions. For instance, a bank application operates with many other applications, namely, Bill Pay utility, trading accounts and business workflows to support various transactions and interconnected activities.

It involves Batch Processing and Real-Time processing, where the transaction processor can be a large capacity mainframe or a legacy system carrying out trillions of transactions per second. Resulting in processes that make the overall financial applications complex.

Summing up, following are the characteristics that enable a robust Testing strategy:

  • Multi-layered functionality to manage concurrent user sessions
  • Large scale integration for multiple activities
  • Real Time and Batch processing
  • Higher rate of transactions per second
  • Detailed reporting to track each activity
  • Strict auditing to handle customer issues
  • Disaster Management mechanism/robust back-up plan
  • Extensive storage system

Multi-layered interactions of a Banking application may involve:

  • End users interacting with the Web Server via a browser
  • Middle tier software that authenticates the input and output for Web Server
  • Database that stores data and processes
  • Transaction Processor to conduct several transactions per second

What are the essentials to consider while testing financial/banking applications?

Security Testing

With reference to customer/user experience and secure interface, security testing ranks high. Traditionally, security testing is considered towards the end of the testing cycle. However, with new-age challenges and malware infecting the financial domain, Security testing has come to the forefront.

With millions of transactions happening every second, stability and robustness of the financial app is absolutely critical. A single security breach can lead to long-term impact on the overall sector, losing out on credibility of the overall system.

Additionally, integration with third-party applications, emerging digital commerce platforms, complex workflows, and growing nexus between Social Media and mobile platforms is making financial apps vulnerable to threats from various sources and various ways.

So, protection of financial data from malicious attacks is imperative to prevent loss of credibility and recurring financial loss. Despite rising number of security boosting products in the market, there are growing incidents of security breaches. Security testing helps make your applications robust and secure for the market challenges. It helps fight the rising and emerging vulnerabilities in the environment.

Security Testing is one of the major steps in the overall Application Testing Cycle. It ensures that the application complies with Federal and Industry standards and gets rid of web vulnerabilities that can expose critical data to a hacker or malicious attacker.

Performance Testing

What if the mobile banking application installed on your device refuses to integrate with your insurance provider, resulting in failure and delay in premium payment? Yes, this could be disappointing and inconvenient for a user.

This drives the need for performance testing applications to boost and ensure customer satisfaction. With financial services institutions constantly expanding across segments and markets, it is important to ensure that the application used by the end customer can take the load and ensure the desired outcome.

Performance Testing/Engineering can help predict, test, and handle loads during critical situations to avoid breakdowns. Further it ensures performance, scalability, resilience, and reliability of the application. Today, financial institutions are venturing in the marketplace with complex application that requires rapid application development cycles.

At the same time, it is important to ensure that the quality of the application is not compromised. Performance Testing brings all this together:

  • It helps monitor and report activities
  • Boosts productivity
  • Brings down the costs resulting from defects
  • Cuts down-time and ensures customer satisfaction

Functional Testing

Functional testing involves Application testing, System integration testing, Regression testing, and User Acceptance Testing. Banking software/applications deal with sensitive financial data and does complex calculations in the background that involve money transfers and highly sensitive data. So, it is important to execute end-to-end functional testing of the application.

What does Functional Testing of banking/financial applications entail?

  • Test cases: This involves listing down the functional requirements, where every business scenario involves a few positive and negative test cases.
  • Verification of test cases: This involves verification of the elaborated test cases in line with the business scenarios, ensuring that every business scenario is covered.
  • Executing functional tests: The tests are involved with basic knowledge of finances and accounting, where either manual or automated testing is put to work.

At Gallop, we understand that Security of your applications is critical for your business and above all how critical it is for the overall financial services sector. One of the top automobile financing firms in the US partnered with Gallop’s Security Testing services to create hack-proof applications.

The core challenge and requirement of the client was to keep the applications secure. The client reached out to Gallop for penetration testing of their flagship web application. One of the major challenges was manual execution of security tests by complying with stringent timelines and regulations.

Focusing on the client’s business objective, Gallop experts executed extensive security assessment tests for the web application to identify security loopholes and vulnerabilities. Apart from the other important aspects of Security Testing, the team implemented custom execution methodology based on the application’s technology and business logic to accelerate manual security testing.

Apart from the fact that the client’s business objective was served, it saved the brand from collateral damage and fixed some major vulnerability. A thorough Security testing strategy further instilled added confidence amongst the end users.

Gallop team has worked with acclaimed players in the sector and understands its intrinsic challenges. Our unique Managed Security Testing Services model combines the deep understanding of industry best practices and decade long expertise in software testing services delivery. We collaborate with businesses in North America to identify vulnerabilities and fix them way ahead in the application test cycle.

With the world economy going through phases of evolution, challenges faced by the banking/financial services sector are endless. Connect with Gallop experts to build a comprehensive testing strategy to make your financial applications secure and reach out to your end users with confidence.

The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

Is your Enterprise Big Data Tested?

Is your Enterprise Big Data Tested

The Startup buzz is gaining grounds and it has transformed the way enterprises strategize and operate. Startups are known to leverage various technologies that boost cost effectiveness, efficiency and time to market. For instance, thanks to the Open source platforms, today Startups have access to the best Big Data infrastructure and testing tools at ‘zero’ cost. They run a mile further in optimizing the Cloud to reap the most from their Big Data investments.

Big Data implementation for enterprises can work wonders. What you need is a robust application that is rigorously twisted and tested to fit your organization’s requirements and objectives.

IDC (a market research firm) estimates 50% increase in revenues from the sale of Big Data and business analytics software, hardware, and services between 2015 and 2019. Big Data and Analytics Sales are expected to reach $187 Billion by 2019.

How does Big Data Empower Businesses?

Big Data has proved to be a game changer for American retail stores, as they have been able to further analyze and effectively segment the customer database and market. This has enabled to create customized marketing campaigns and offer relevant deals. Further, they have been equipped with information to schedule their deals and offers as per the data drawn by the application.

It is further predicted that government organizations across the globe will leverage Big Data to radically reduce government expenditure. High profile statisticians and officials will be replaced with Data Scientists to derive the required numbers.

After the super successful and intense Climate Change talks in Paris, there is going to be a whole lot of difference in the way Climate Change is perceived. It will not be alleged as a matter of threat, but an enabler for Market Capitalization purely on the basis of Big Data technologies. For instance, Big Data will analyze climate change views and expert comments across Social Media and Internet, which will help determine the impact rather than just depend on the conventional Meteorological reports.

Big Data implementations have brought remarkable results for enterprises who knew and kept their conviction towards the business objectives. However, it can be a major disappointment for organizations that miss out on the underlying purpose of Big Data implementation.

If the data is managed methodologically, it can empower an organization to make informed choices while venturing in the market place.

What does Big Data Testing Entail?

Big Data testing involves authenticating various data processes and not just the features. Performance and Functional testing work effectively for Big Data applications. While testing their applications, QA engineers process three types of data – Batch, Real time, and Interaction.

Collaborating with an experienced testing partner is absolutely key, as it is important to devise a high level test strategy. Moreover, before the testing starts, it is important to check the data quality and confirm related factors like data accuracy, duplication, and validate whether the existing data is all-inclusive.

In this article, we would like to highlight some prominent benefits of Big Data testing, assuring desired results that can enable informed decision making and ensure higher ROI.

Eases Downtime

The emerging concept of Bring-Your-Own-Device (BYOD) and implementation of Cloud services facilitates anytime, anywhere access to enterprise applications. Due to this there is a rising dependency on the organization’s data to run these applications. This sometimes affects the performance of the application. So, it is important to test the Big Data applications that are expected to be available for employees 24*7. It will avoid bugs, enhance data quality, and ensure seamless functioning of the application. In summary, reduce any expected downtime.

Eases Operating with Large Data sets

With Big Data Applications, development begins with implementation of small data set and then moves on to the larges data sets. As expected, the glitches occurring with small data sets are way lesser than with larger ones as the development process matures. With a view to avoid breakdown of enterprise level applications, it is crucial to test the application’s lifecycle and ensure flawless performance irrespective of changes in data sets.

Maintains Data Quality

Integrity and quality of data is immensely vital for an organization’s growth and attaining overall business objectives. Big Data is increasingly getting popular today, as it empowers enterprises and top management folks to take informed decisions based on historical as well as contemporary data points. Testing these business critical applications helps you avoid duplicity and redundancy with the data sources.

Strengthens Credibility & Performance of Data

The effectiveness and performance of Big Data applications depends on the accuracy and authenticity of the existing data available within an enterprise. Big Data testing involves verification of these data layers, data sets, algorithms, and logic. This efficiently ensures performance of business critical Big Data applications.

Authenticates Real-time data

As mentioned earlier, real-time sourcing of data defines the effectiveness of Big Data application for enterprises. Performance testing of the required data is important to confirm its operational efficiency in real-time. Time is the key word and testing is the only mechanism to determine the ‘time’ factor.

Digitizing data

Organizations across the world have data stored in hard copies, which needs to be cleaned and digitized. Testing helps to scrupulously assess and ensure that no data is not corrupted or lost. The data is converted into various digital formats as per the organization’s requirements. This further ensures availability of essential data in real-time and optimize the processes.

Checks Consistency

When data is digitized, it gets converts into various formats. With Big Data applications and predictive analysis, there are chances of inconsistency over a period of time. Testing brings down these disparities, thus reducing uncertainty.

A comprehensive Big Data and Predictive Analytics strategy enables enterprises to be more analytical in their approach, ensuring higher ROI. Today, enterprises are rapidly seeking Big Data and Analytics solutions. It is predicted by market research firms that the utilities, healthcare and BFSI sectors will bring fastest revenue growth in Big Data and Business Analytics.

Collaborating with the right partner is the need of the hour. Gallop has worked with global enterprises to devise a resourceful Big Data Testing strategy. Connect with our experts and understand the various facets of Big data testing.




The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

The Challenges of Testing in a Cloud Computing Environment

The Challenges of Testing in a Cloud Computing Environment

Cloud Services, as the experts say, are here to take enterprises from the Industrial era to the era of Information Accessibility.

What could be the most apparent instances of operating in a Cloud-based environment?

  • Updating your Mobile Applications on the go
  • Mailing / accessing some work documents outside your office premises
  • Updating your Facebook & WhatsApp status while commuting
  • Accessing your bank accounts and making transactions while you travel

These and many more benefits are today enabling small, medium, and large enterprises to solve small as well as big challenges to reach their highest potential.

Additionally, there are some evident reasons why big and small enterprises across the world are moving to the Cloud. Namely, higher efficiency, flexibility, disaster management / recovery, easy updates/upgrades, better collaboration, anywhere/anytime access, competitive edge; additionally, it also encourages to make the work environment more eco-friendly.

Sig Nag, research director at Gartner aptly states, “The market for public cloud services is continuing to demonstrate high rates of growth across all markets and Gartner expects this to continue through 2017”. With the trending growth of start-ups globally, there is a rising need to cut down infrastructure costs, faster time-to-market, and achieve higher ROI. These are amongst the most prominent reasons for the increasing popularity of Cloud services globally.

According to a recent Gartner report, Worldwide Public Cloud Services Market is estimated to reach $204 Billion in 2016. The highest growth will come from cloud system infrastructure services, which is projected to grow by 38.4 percent in 2016.

There is rising trend amongst enterprises today to adopt a digital business strategy, as the focus is shifting from redundant IT systems to a cloud-based environment.

Market reports estimate that as many as 90% of the organizations in UK are implementing Cloud-based services to meet their specific business objectives.

At the same time, the Cloud Computing environment poses a set of challenges, which organizations have to deal with. While this article intends to deliberate on the testing challenges in the Cloud Computing environment, it is imperative to get a quick grab on the challenges that the Cloud poses.

Mentioned below are some highlights:

  • Security Aspects: While the Cloud environment offers you a ready back-up and a robust disaster recovery plan, there has been a persistent concern over the security aspects of hosting your data on the Cloud. It ranks amongst the most prominent concerns mentioned by users, as an essential chunk of services are outsourced. This results in risks like data integrity and availability of services.
  • ROI Estimation: In spite of the fact that Cloud ensures higher ROI, there is a concern amongst business folks about the commercial viability or business outcome of Cloud adoption. Nevertheless, this aspect can be addressed by mapping the same with the organization’s objectives of adopting Cloud services.
  • Quality of Service: Considering specific services on the Cloud are outsourced to third parties, businesses are sceptical about moving business critical application to the Cloud. There are concerns pertaining to factors like availability, accessibility, performance, and scalability.
  • Performance Related Issues: There is a concern regarding the network bandwidth offered by Cloud services. This may negatively impact execution and delivery of complex application, as Cloud applications still continue to be bandwidth intensive.
  • Integration Related Concerns: Some applications in production need to integrate / connect with other Cloud applications, which could be on-premise. For instance, applications that are currently operating with existing data structures. There is uncertainty amongst businesses regarding the interoperability of these applications hosted on the Cloud.

While robust testing frameworks and strategies help enterprises get the most of their investment in the Cloud, there are a set of challenges related to testing on the Cloud. The foremost factor to focus on while testing is to ensure that the specific functional requirements are addressed. Let’s take a quick view of the 2 fundamental aspects of testing in the Cloud environment.

  • Functional Testing: With this you can evaluate and ensure that you are getting value for your buck. It further confirms that the business requirements are met. Functional tests such as System Verification testing, Acceptance testing, and Interoperability testing ensure that the business requirements are being met.
  • Non-Functional Testing: The Non-functional testing approach focuses on web application based tests that help ensure that all the expected outcomes are met. Some key areas of non-functional testing are: Accessibility, Performance, Security, Recovery, and Scalability tests.

Challenges of working in the Cloud environment intersect with the challenges of testing applications on the Cloud. The reason being, it entails testing applications across various modules and environments to identify issues.

The recommended way out is to utilize test services that operate on the Cloud, which provide the required environment for testing.

Depending on the Cloud for performance and all the possible boons is a choice that enterprises are making consciously. Drilling down, let’s focus on comprehending the testing challenges in order to address them effectively.

  • Developing the environment: There is a need for specific configurations for testing purposes, for instance, related to servers, storage, or network. This leads to issues during testing, as it is a challenge to emulate the specific customer scenario / environment in the Cloud environment.
  • Interaction between components: Another challenge that is commonly faced is during integration testing, where the tests are conducted on the network, database, servers, and various devices. When the applications are hosted on the Cloud, testers have inadequate control on the environment. This further amplifies when there is an interaction between all the components, where there could be risks pertaining to crash, breakdown of the network or crashing of the servers.
  • Replicating the user environment: The business applications to be tested are installed in an environment that is not under the control of the tester. The tester is expected to test the applications in a scenario where the user would operate. This poses a challenge for the testers to create a similar environment for testing purposes.
  • Testing on the Internet: There are a lot of security concerns while testing on the internet. The tester has to ensure that there is no leakage of data that is available over the Internet while the testing is in process. It is important to have a back-up plan and test before the business applications are released for the users.
  • Testing across the spectrum: Cloud Testing entails testing the network, performance of the server, the available database, and the application. Keeping these requirements in perspective, the testers need to validate the interaction across various components and layers. At the same time risks have to be anticipated, for instance, loss of connection, crash of the software, etc. Moreover, the tester needs to anticipate and test beyond the aspects under control in the existing environment.

So we infer…

It is imperative to comprehend and address these challenges with a clear perspective to ensure that the various business applications are operating effectively and securely on the Cloud. Moreover, it is important to confirm that the entire process helps gain competitive edge in the marketplace. Irrespective of the existing challenges, Cloud Computing will sustain and empower organizations to stay ahead and continue to remain competent.

The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

Moving from Descriptive Metrics to Predictive & Prescriptive Metrics

Descriptive metrics to predictive and prescriptive metrics


With the deluge of data being churned every day in businesses, organisations are turning to analytics solutions to understand what these huge volumes of data mean, and how can they help improve decision making. We need to chart a new course with data, which is to predict.

Every organization that is driven by data wishes to fulfil its promise of doing it right. Reviewing the available analytic options in itself can prove to be a humongous task in itself. Analytics are necessary when data is needed to answer specific business-related questions, whereas through metrics it’s being responsible to a certain action and in order to measure, metrics are formulated from the analytics available.

The analytic options can be categorized at a high level into three distinct types, Descriptive, Predictive, and Prescriptive.

Descriptive Metrics is raw data in summarized form. It uses the techniques of data aggregation and data mining to provide meaningful insight into the past and answer: “What has happened?”.  For example, the no. of Defects, Testers, Iterations and other metrics that show what’s happening in testing department in an easy-to-understand way. Descriptive metric explains a specific aspect of a system. For instance, if we were trying to automatically analyze a web page, we may want to rate the legibility of its text based on the font size being used. To identify the font size being used–a descriptive metric– and report this value, an automated tool may be used. The metric also helps identify the modal font sizes for other websites without claiming any implications of the value. This is in contrast to a predictive metric.

Predictive Metrics use forecasting techniques and statistical models to understand future behaviour by studying patterns and answer: “What could happen in the future?”.  For example, Code Coverage, Defects in future, etc. Predictive metric describes an aspect of a system for providing a prediction or estimate of its usability. For example, if the font size used on a web page is used as a predictive metric (for example, observing that a larger font size is more legible), a designer may assume that as a larger font increases the page ranking, it will also increase the usability of the design. This is in total contrast to Descriptive metrics that do not make any explicit claims as to the implications of the measurement.

Prescriptive Metrics use simulation algorithms and optimization to advise on the possible outcomes and answer: “What should we do?”. For example, efficiency, effectiveness, risk-based testing, and increase code coverage.

Predictive vs. Prescriptive Metrics

Predictive and prescriptive metrics can be drawn from descriptive metrics which give insights and decisions. Effective business strategy can be chalked out with both types of metrics. Predictive metrics by themselves are not enough to beat the competition. It is the Prescriptive analytics that provides intelligent recommendations for the next best steps for almost any business process to achieve the desired outcomes and to increase ROI.

While both types of metrics inform business strategies based on collected data, the major difference between predictive and prescriptive metrics is that while the former forecasts the potential future outcomes, the latter helps you draw up specific recommendations. In fact, Prescriptive metrics uses Predictive metrics for arriving at the different options available along with their anticipated impact on specific key performance indicators.

There’s an interesting example in an article published in Business News Daily. It states “For example, a nationwide sporting goods store might use predictive and prescriptive analytics together. The store’s forecasts indicate that sales of running shoes will increase as warmer weather approaches in the spring, and based on that insight, it might seem logical to ramp up inventory of running shoes at every store. However, in reality, the sales spike likely won’t happen at every store across the country all at once. Instead, it will creep gradually from south to north based on weather patterns.”


It is obvious that for getting the most accurate predictive and prescriptive analytics, the data available needs to be kept constantly updated as per real-time successes and failures. As someone mentioned, “The analytics are only as good as the data that feed them.”

To know more about these burning topics, attend a Webinar on ‘Accelerating Digital Transformation Journey with Digital Assurance QA’ on 20th July 2016, 11AM EST by Sai Chintala, Senior Vice President, Global Pre-Sales. Reserve your slot here.


The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

Need for Innovation in Digital Quality Assurance

Need for Innovation in Digital Quality Assurance1

Going Digital is no more a new word in the technology arena as most of the IT organizations have assimilated themselves to the new trend. The wheels of digital revolution have pulled every organization towards it, as it leads to continuous, consistence interaction with customers across the multiple channels leading to consumerization of services.

Digital transformation involves mobile, customer experience, social media and big data. Most of the organizations unraveled the ‘apps race’ as a part of their digital transformation strategy focusing on delivering high quality, secure user experiences, with assured business outcomes. This led to growth in IT spend allocation to QA and Testing to 35% till 2015 and might increase to 40% by 2018 according to World Quality Report 2015-16. Demand for greater agility, shorter lifecycles of device and services, and integration of services across platforms increased the importance on quality assurance testing

It might be easier for the “Born Digital” organizations to view QA as an integral part of their growth graph. But for the organizations performing testing on legacy and web application, and who are currently in the transformation stage, digital testing unless until coupled with innovation would be difficult to achieve the desired goals of customer centricity. Therefore, it is essential for the existing QA organizations to understand the current testing trends and innovate ways to provide effective testing solutions.

Digital Quality Assurance Challenges include:

Continuous Delivery: The need to meet end user expectations made continuous delivery essential. In continuous delivery environment, testing is done in small increments at frequent intervals with code integration as its being build. This would help in early detection of problems and determines the effectiveness of change.

Complete coverage: Identifying the end-user requirements and defining the right coverage of their expectations would be a major hurdle where it requires a constant communication or interaction with target segment.

Test Case Design Strategy: Use of agile scrum methodology to develop the custom apps might be an efficient way to achieve fast time to market, but a test case design following a similar strategy will have to be assessed and defined such that it accelerates the test life cycle.

Test Automation: Test tools should complement the agile development process of the application. The test automation scripts should reflect the latest version of system for which they have been developed. When the system changes, there is a need to change the tests to suit. Otherwise, the maintenance of automated test scripts outweighs their effectiveness in an unstable environment.

Mobile Labs: Demand for digital testing exploded as the use of mobile devices have become an integral part of customer existence. Although most of the organizations agree that time is one of the important factors for QA and testing in mobile development, testing across plethora of mobile devices and enhancing the customer usability is a challenge in itself. Even though organizations tend to have set up real mobile device labs as test environment, this just adds to fixed cost of maintaining them as the life cycle of mobile has been changing exponentially.

Establishing of test environments: To provide effective customer experience, the solutions have to be consistent across different channel interfaces. As the channel interfaces gradually increase, testing and maintenance of those environments would be a huge hurdle. In view of ever changing test environments, use of cloud technologies in the establishing test environments might seem a plausible option to avert the infrastructure costs and diversity of mobile devices.

Generation of dynamic test data: Static test data is an element of past! As more and more mobile applications are being built, the need to test the software components as they build has become an existential need. To accelerate testing across the latest applications, there is a need to generate dynamic test data to stabilize the applications and bring a close proximity to customer behavior.

Non Functional testing: As the goal of consistent customer experience across the channels has to be achieved, there has to be a special emphasis on performance and security of the customer data, which flows across channels. The risk of losing customer data across multiple channels and multiple platforms is huge, therefore the Security assessments, vulnerabilities, and performance audits of the environments are essential to boost the ability of applications, infrastructures and end-points. There has to be a holistic view of performance from server to front-end applications.

The challenges in digital testing is pushing the QA organizations to innovate ways to address them and provide the best in class solutions. With customer experience as the core pillar, organizations needs to deliver on the following aspects to stay relevant: Customer centricity with analytics based assurance, agility with faster time to market, stability with best in class QA practices and future ready applications with enhanced performance.

Are you looking to know more about our Digital Assurance services? Then, don’t miss the Digital QA Webinar on 14th July,2016 to know how to accelerate your Digital transformation journey.

About the Author:

gallop-software testerAbout the Author: Naresh is a Solutions Lead , part of Enterprise Solutions Group at Gallop Solutions. Naresh supports Sales and Delivery teams in creating technical proposals and supports bid process. He hold a PGDM degree from T.A.Pai Management Institute, Manipal and graduated(B.E) from Osmania University, Hyderabad in the stream of Electronics and Communication Engineering. He has keen Interests in latest technologies, Psychology and Philosophy.
The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

Customer Experience Testing in Digital World

Customer Experience Testing in the Digital World

Digitization has pervaded almost every aspect of our personal as well as professional lives. This has made it necessary for the businesses to embrace and execute well-defined and well-planned Customer Experience Testing strategies in order to keep the customers loyal, satisfied, and happy.

Difference between User and Customer Experience

The organizations need to differentiate between User experience and Customer experience – the two terms are used interchangeably many-a-times. They also need to focus on developing strategic plans by properly mapping an individual’s interactions with the various products/services that they use. To design an effective Customer Experience Testing strategy, complete understanding of customer behaviour (both intrinsic and latent characteristics), along with tangible and functional traits is of paramount importance. Also, non-functional requirements and aspects like capability, ease of access and use & navigation should be considered in devising the test strategy. There is also a need to address intrinsic human behavioural elements from end-customer experience perspective like cultural fit, psychology and emotions.

The relevance of end-user experience

While the digital products have already evolved in both the B2B and B2C segments, the “direct-to-customer” model is also gaining high momentum. This makes end-user experience even more relevant and it must be correlated with enhanced user adoption, increased customer loyalty, and social conversions achieved. Hence, there is a business-critical need for robust testing strategy that suits the needs of CIOs and CMOs, and also has a transformational impact on the business and revenue models.

In order to achieve this, an effective Customer Experience Testing strategy should consist of better design, and efficient digital channels with real-time analytics to achieve user goals and fulfil business objectives.

Important Factors in Customer Experience Testing

The factors that play an important role in Customer Experience Testing are:

  1. Emotions: Social channels can be used to capture human emotions through social listening techniques and covering the findings using real-time analytics, sentiment analysis, etc. This will help the organizations in identifying the defects not found during UAT.
  2. Crowd’s Wisdom: Crowdsourcing can be used as a means to pre-launch end-user feedback by choosing a demographically distributed crowd which is culturally and socio-economically similar to the end-user.
  3. Analytics: Performing a varied set of activities like personalization, keeping a track on multi–channel and cross-channel browsing history, transactions, monitoring likes and dislikes across channels, etc. will play a crucial role in ensuring a seamless Customer experience in the digital world.

The Available Testing Options

Digital customers have the option to choose from a variety of products, applications, channels, etc. Addressing the needs of variety that customers have (- and demand) needs accelerated efforts by consumer-facing companies to achieve end-user delight. Resolving the issues in an effective manner impacts the top line directly as the transactions are linked closely to brand loyalty, word-of-mouth publicity, and repeated purchase by the customers. Hence, defining and executing an effective Customer Experience Testing gains prime importance across the product development lifecycle as well as during the post-production phase.

There is a need to enhance traditional test case-based approaches by adopting techniques that include automation, overall end-user journey, real-time user analytics, and persona-based strategies along with Omni-channel interactions across various digital touch points and connected devices. The products/services can also be tested by disparate crowd in demographically specific regions, most suitable for particular end-user situations. Customer Experience Testing also needs to be a continuous process as the expectations of end-users for products/services are continuously evolving with multiple changes happening simultaneously in the Digital world.

Gallop’s state-of-the-art test labs use the latest and best in testing to bring you better quality and quicker time-to-market. Our fine-tuned testing services are supported by our proven testing approach, Proprietary Testing IP (ETAS), and capabilities through our Center of Excellence. Discover the Gallop advantage today.
About the Author:


gallop-software testerAbout the Author: Abhijeet Srivastava is an Associate Manager at Gallop Solutions. He is a part of Enterprise Solutions Group which primarily helps convert Leads to Deals by devising the best solutions. He holds a B.Tech in Electronics & Communication Engineering from Sikkim Manipal Institute of Technology and PGDM from TAPMI, Manipal. His Core Skills are Business Analysis, Sales pitch, Architecting Solutions, building Proposal, etc.
The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

SaaS Testing: Challenges and How to overcome them

SaaS Testing: Challenges and How to overcome them

SaaS or Software as a service is gaining a lot of momentum and wider adoption by organizations as they are realizing the real benefits by using SaaS over On-premise installed applications. In SaaS model, the organization need not pay for the software or hardware itself, it’s more of a rental scheme where they pay as they use. This is what makes SaaS attractive compared to the On-premise option.

It’s a tough decision for the organizations to choose SaaS, as there are lot of factors like complexity of system, application stack, and operational aspects which needs to be considered. Especially for the enterprises with legacy applications, it’s a very tough decision considering the investments they have made in their own datacenters. Few of the factors which comes into play while choosing SaaS applications are Security, Return on Investment, Platform suitability, Compliances and Integration.

These factors coupled with other challenges necessitates the need of SaaS testing.

So what is SaaS Testing?

SaaS Testing refers to the set of testing methodology and processes used to ensure that applications built using the software as a service model of development, functions as designed. SaaS applications entail thorough testing for their integrity, different from that of on-premise applications. This involves testing of data security and privacy, business logic, data integration, performance, interface compatibility, Optimization of testing, and scalability, among others.

SaaS testing also have shorter testing cycles because of the architectural model of software delivered as a service, as compared to traditional software delivery. SaaS testing methodology thus does not require test cases for client or server installations, multi-platform back-end support, multiple version support or backwards compatibility usually. But there are many other different test cases which comes into play for SaaS Testing because SaaS applications function in a cloud computing environment that incorporates SOA (service oriented architecture) and Web Services into the fold.

Also agile methods are typically part of SaaS testing because of the speed of delivery. The use of test automation tools for building regression suites in this agile model helps organizations bring in business value and quickly validate the impact of upgrades also.

So let’s have a look at what needs to be tested for SaaS Applications?

  • Performance Testing: Performance is the most critical factor for the SaaS applications. Each module of the application needs to be performance-tested along with the workflow. It’s also up to testers to determine the throughput expected in the workflow. Also by stressing the system with load tests, the team can determine the application’s ability to handle unsteady loads and find the maximum supported levels. SaaS testing, with a focus on ensuring performance, is imperative to a SaaS provider’s success.
  • Availability Testing: Making sure that the application is available at all times for the users is very important for successful testing. The SaaS application should not go through any downtimes.
  • Security Testing: This is major concern and sometimes also a deal breaker if you are opting for SaaS option for applications. It is vital that proper security testing is carried out and any threats to the data, privacy does not exist.
  • Interoperability Testing: Every SaaS application must be able to function seamlessly in all different environments and platforms so that users from all backgrounds can use them.
  • Stress and Load Testing: SaaS application needs to be tested for various amount of stress and load beyond its usual operational capacity in order to evaluate how it responds to and gives results.
  • Integration and migration tests: There are many API’s to which your SaaS application might be integrated with. The data migration and integration should be checked and tested while ensuring the data privacy and security.
  • Business workflow tests: Business workflows and other competent functionalities needs to work as planned. Knowing the different configurable and non-configurable components of the application makes it easy to test and get the best out of the application.

Though SaaS testing comes with its unique set of challenges, the right skillset and planning can help mitigate risks associated with it. Gallop’s SaaS testing methodology ensures that right strategy, automation & best practices are followed throughout for your application on cloud.

The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

Testing Machine to Machine interactions in IOT World

Testing Machine to Machine interactions in IOT World


M2M or Machine to Machine interactions have been around for quite some time now and with IoT getting into mainstream, the machine to machine interactions are at the forefront again.

So what makes IoT and M2M so interesting?

As per GSMA Intelligence, there are now more than 7.82 billion devices, including M2M devices, which is way above the number of humans pegged at 7.4bn. We are working in a world where the importance of connected devices is getting more relevant and with the amount of automation happening, we are going to see a huge jump in the connected machines by next decade. As per Cisco, “the Internet of Things (IoT) will have up to 50 billion things (or devices) that will be connected to the Internet by 2020; or, the equivalent of 6 devices for every person on the planet.”

Though M2M has been there since 1930s, when the British military first invented radar to detect aircraft, it is getting into every household these days. M2M is at the heart of Internet of Things and with connected devices getting so common, in future, it might be at every corner of your house. IoT is all about connected devices communicating with each other in form of data which is being analyzed and action being taken in real-time with/without human intervention. Given the current pace of things, human intervention is going to get less every year.

As more and more devices start taking decisions without human intervention, testing and certifying such devices will become more critical for the organizations. Some of the factors which needs to be considered are:

  • Skill set:

This is going to be the most important factor for testing M2M/IoT applications as the resources are supposed to have domain knowledge as well as systems engineering understanding. Having the right resources with good skillset is going to be critical for success of IoT projects.

  • Test environment management:

Having the right test environment for testing the devices, applications around it, communication between machines is going to be difficult as the number of devices rises. Once the devices with different versions and upgrades emerge, mimicking and simulating the test environment will be a challenge.

  • Test data management:

Here comes the real challenge. M2M usually generates terabytes of data for different processes, and which may also be behaviorally different. And given that there will be different types of devices which will be communicating and generating different types of data, getting it right becomes even more important.

  • Security testing:

Data privacy and application security are the non-avoidable types of testing, given that there will be lot of loopholes when multiple devices interact over multiple channels. This becomes even more important because m2m devices usually don’t have specific identities (currently), and testing thus them for vulnerabilities is must because the amount of data which gets exposed.

  • Compatibility testing:

IoT has given rise to different operating systems, devices and messaging protocols. Making sure that different devices communicate properly while maintaining the standards is key to success of the IoT.

  • Performance testing:

Performance becomes critical in the case of IoT as the response time between machines is critical to the success of the business scenarios which run over these devices. At the same time, measuring the vital statistics of devices like power usage, memory usage, endurance testing, disaster recovery testing is also important.

  • Accessibility testing:

Testing for accessibility is going to grow as we see more interconnected devices around us. With smart cars talking to smart buildings and in turn passing the information to smart wearables, this is going to be compulsory type of testing to be considered.

  • Regulatory compliance testing:

We are going to see standards, protocols and compliances emerging in the IoT space and making sure that regulatory requirements are adhered to will be crucial.

These are few of types of testing which we feel are important. But there will be many more types of testing which needs to be considered to cover the end-to-end functionality of the IoT devices.

It is going to be interesting to see how this space evolves and if any new standards, tools and, processes becomes part of the IoT software delivery pipeline. Whatever it will be, we will keep you updated with the latest in IoT testing world.

The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

Successfully Implementing TDD/BDD to Enable Shift-Left Testing Approach

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Today, when developers are using tools like J unit/N unit for testing their code, approaches such as test driven development (TDD) and behaviour driven development (BDD) focus on improving the quality of the code that is being written. Though the approaches require a different mind-set, the objective remains the same.

Usually Behaviour driven development is focused on the business behaviour of your code: the “why” behind the code. The focus is usually on the intent rather than process. It supports a team-centric (especially cross-functional) workflow. BDD works really well when a developer and either the agile product owner or a business analyst sit down together and write the pending specifications:

  • The business person specifies the exact functionality they want to see in the system.
  • The developer asks questions based on their understanding of the system, while also writing down additional behaviours needed from a development perspective.

Ideally, both parties can refer to the list of current system behaviours to see if this new feature will break existing features. This way quality comes first and the entire product is understood which decreases the defect entry into the development related to requirements or functionality of the system.

Test Driven development on the other hand focuses on implementation of the system. For example, writing a better quality code that helps in maintaining the health of the code with no nonsense into the system.

Approaches like TDD/BDD are used to understand the requirements clearly without any ambiguities and help developers to write tests in a way that makes code execution successful. These methods enable testers to think of solutions using a bottom up approach that helps prevention of defects in the later stages. This approach also helps clarify any ambiguities in the requirements early on in the software development lifecycle, before coding actually begins. With an increased level of understanding of the features and requirements, developers know what exactly needs to be coded, as also what needs to be included or excluded in the code, thereby preventing leakage of defects into the code in the later phases of development lifecycle. The mindset and ability to focus on producing quality product with minimum to no defects from inception/upstream process is enabled by these methods that complement the shift left approach.

While the development teams like this approach, the project teams blame the TDD/BDD process for slowing down the software development process. However, it has been realized that implementation of TDD/BDD practices in the initial development phases enables organizations face lower defects in the later stages. This helps in the maintenance of the code, increases the defect removal efficiency, and also reduces the time to market of the product. The TDD/BDD approach is also best suited for applications where the requirements undergo progressive elaboration. The frequently tested code has lesser defects and enables faster delivery of working software to the clients.

Practices like unit testing and adopting TDD/BDD provide high code coverage coupled with faster feedback regarding unexpected defects/surprises/issues and thus becomes an additive element in the reinforced process.

TDD/BDD practices also enhance practices like requirement management for covering the finer topics like requirement elicitation, requirements acceptance criteria, and requirements review prior to the development process. Requirement traceability is also enhanced when test cases are traced back to the requirements giving a picture of test coverage functionally.

A seamless implementation of both approaches identifies defects early on in the SDLC process, reduce risks, and reduce cost of rework which is a significant cost in software development process. TDD/BDD helps align the mind-set to the left focussing on quality from concept-to-cash for building the right product with the right intent in the best possible way.

In a nutshell, the BDD/TDD practices enable the following:

  • Move defect identification and prevention to the left (early stages of SDLC)
  • Reduce issues/surprises/incidents in the production
  • Help teams stay focused on Continuous Delivery
  • Compliment the agile/iterative development
  • Improve the overall build deployability by reduced lead times and increased quality
The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

Digital Assurance and need of Omni Channel Testing

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Assurance refers to a positive declaration that instills confidence or a sense of surety.

In today’s world where everything is being digitized or automated, it is very much necessary that the end users of the products get a feeling of security and assurance that their data and other personal information that they might share on the web is safe. This is what Digital Assurance is all about. Digital Assurance refers to assuring the customers that none of their personal data is vulnerable to being exploited by hackers. This in turn ensures maximum customer satisfaction.

Now a days as digital technology is covering all spectrums of business solutions there is a rising demand for almost a completely flawless customer experience and safety. Digital Assurance aims at meeting this demand. Organizations providing a holistic assurance strategy not only ensure successful digital transformation but can also optimize their IT budgets.

Digital Assurance refers to Q&A practices that ensure the relationship between the various components of Digital Ecosystem remains smooth. The Digital Ecosystem includes various interconnected people, processes & things cutting across the Social, Mobile, Analytics and Cloud stack.

Here are the few reasons why organizations need to take up Digital Assurance.

  1. Need for being Agile: Being agile through continuous quality assurance initiatives, and automating processes to ensure shorter delivery cycles become highly critical in a highly dynamic digital landscape.
  1. Make or Break the Customer Experience: Delivering enhanced customer experience while leveraging a variety of components of Digital Ecosystem becomes challenging. Ensuring each component delivers optimum performance leading to customer delight becomes difficult as the organization becomes more digitally matured.
  1. Organizations are vulnerable to Security Threats: Security is of paramount importance specifically in an interconnected world. The smart interconnected ecosystems on one side enable an unimaginable world of possibilities, but it makes the entire system highly vulnerable to security threats, if not properly configured and tested, on the flip side.
  1. Performance of Organizations Legacy Infrastructure needs to be maximized:This is an important challenge specifically with those enterprises who are not born Digital. They need to change their organizational DNA both from a cultural perspective and from legacy IT infrastructure standpoint. Ensuring their core functionalities are not impacted as they chart their path towards Digital Infrastructure can be much challenging.
  1. Complexity: The most difficult challenge comes from the complexities arising from the nexus of forces –Social-Mobile-Analytics-Cloud, to ensure that they work in synchronization with organizational goals.

We need to consider the following important points while implementing Digital Assurance: 

  1. Omni-Channel Assurance: Omni-channel testing is based on preparing a test strategy with a view of all the channel and user interaction patterns.
  1. Users spread across the globe: Internet users are spread across the globe with around 46% of the population having access to the internet. The region wise statistics are: Asia-48.2%, Europe-18%, Latin America-10.2%, Africa-9.8%, North America-9.3%, Middle East-3.7%, and Australia-0.8%. Irrespective of these statistics the users should find similar functionalities, semantics and experience. Therefore Digital Assurance strategies should include both localization and globalization tests.
  1. Validating business scenarios:The Digital Assurance strategies must ensure that the business functionalities and expectations are met by the application.
  1. Customer experience and social integration: The Digital Assurance strategies must ensure a good user and brand experience irrespective of the channel and the other factors.
  1. Security Assurance: Application are being hosted on multiple platforms which has increased the risk rate thus having to necessarily perform security testing for these possible vulnerabilities by focusing on application profiling, authentication, data validation and encryption algorithms.
  1. Lifecycle automation: Let me discuss more about Omni-channel testing in detail.
    Omni-channel is a multi-channel approach to sales that seeks to provide the customer with a seamless shopping experience whether the customer is shopping online from a desktop or mobile device, by telephone or in a bricks and mortar store.What distinguishes the Omni-channel customer experience from the multi-channel customer experience is that there is true integration between channels on the back end. For example, when a store has implemented an Omni-channel approach, the customer service representative in the store will be able to immediately reference the customer’s previous purchases and preferences just as easily as the customer service representative on the phone or the web chat. Also, the customer can check inventory using laptop by store on the company’s website, purchase the item later on with a smartphone or tablet and pick up the product at the customer’s chosen location.The main difference between Omni-Channel and Multi-Channel lies in the process being tested.
    If you want to test a single process that spans across multiple devices, you want OMNI-CHANNEL testing.
    If you want to test the same process on a range of devices, you want MULTI-CHANNEL testing.Omni-channel testing is not just about consolidating testing across channels but designing a test strategy with a view of all the channels and real user behavior.

So what are the top advantages for businesses due to Digital Assurance:

  1. Customer-centric business: This is done by ensuring flawless customer experience.
  2. Agility in business: Automation and virtualization ensures quicker time to market.
  3. Stability in business: Finding out errors in advance and modifying the tests accordingly to ensure the best testing cycle.
  4. Future proofing the business: Anticipating the future and developing methods of minimizing the effects of shocks and stresses of future events.

Are you looking for Digital Assurance services and leverage the advantages listed above for your business? Then, reach out to our digital testing experts for a free assessment.


gallop-software testerAbout the Author: Dheeraj Kumar M is a Software Consultant at Gallop Solutions. He is part of the Innovation Vertical which primarily transforms an Idea into an actual product. Part of multiple Technical workshops right from his college days has helped him shape up his skill sets. His Core Skill is with Core Java, HTML5. He is also an avid shutterbug capturing the picturesque Mother Nature in his spare time.
The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.