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Software products have become an integral part of driving the whole global digital ecosystem. They bring with them attributes like convenience, speed of operations, security, and privacy, among others. As customers became choosy with their preferences and the competition among providers got intense, quality became the core differentiator that helped an organization stay ahead of the competition. To ensure the delivery of superior-quality products from the build pipeline and pre-empt customers from facing any issues with their performance, the former should be subjected to rigorous software product testing. Over a period, quality assurance has come to be associated with standard processes, practices, and methodologies. These should be followed in letter and spirit to enhance quality, streamline workflows, improve efficiency, and be responsive to customer feedback. However, a few QA practices have become dated and should be updated with the new trends. To understand this better, let us discuss some dos and don’ts about software application testing.

The do’s of software product testing

Quality Assurance (QA) is a critical requirement in the product development lifecycle to identify and remove glitches. It helps to make a product competitive and allow it to meet the customer’s expectations. If QA is not part of the SDLC, the consequences for the entire value chain can be severe.

The product would be left with glitches thereby impacting its performance.
Hackers can exploit the inherent vulnerabilities to steal sensitive business and customer information.
Security protocols can go for a toss with the business inviting censure and penalties from regulators.
The brand suffers immensely as adverse publicity through word of mouth can go viral.
Choose specific test cases for automation: Automation is the key when it comes to performing repetitive tests such as regression. However, care should be taken to choose the right test cases for automation as test complexities can render such testing infructuous. To automate everything is certainly not the way to go about in a software product testing strategy. There should be a proper selection criterion in place to choose a test case for automation. If done the right way, test automation can deliver benefits that far outweigh the costs.
Upgrade skills for automation: Even though automation is a potent software product testing method, the skills required to execute the same is not always available. For any team going for test automation as part of the software product testing strategy, the QA team should be well-versed with any one of the programming languages such as Ruby, C#, JavaScript, or Python. Besides, the team members should have the expertise of handling automation test tools like Selenium. The bottom line is that more the testers have skills for automation, better will be the outcome of software product testing.
Quicken the pace of testing: In a non-Agile test environment, testers are wont to leave some of their difficult tests at the end of the SDLC. However, this practice is flawed as testing the quality of applications at the end of the development process hinders delivery. Rather, the testing team should adopt a risk-based approach towards software product testing and execute the high priority cases first.
Manage the testing environment: Testers seeking deployment no longer holds, for today, it is more about managing the test environment by configuring the CI tool or Selenium grid. There should be cloud, containers, and virtualization, and the ability to write appropriate test scripts.
Shorter tests: The test suites should be made shorter to enable better and quicker detection of glitches. Not everything should be tested in a test scenario, for it would make troubleshooting difficult later.
Follow shift-left: The QA team should align itself with the development team through shift-left testing. This way, they can make a better impact on the quality of software and deliver it faster through the value chain. Also, shift-left helps developers to quickly mitigate any glitches in the code and move to the next sprint.
The don’ts of software product testing

In addition to the above-mentioned ‘dos,’ testers should follow some don’ts as well to enhance the quality of testing and not leaving anything to chance.

Tracking defects in many places: Keep a single log of defective cases or glitches instead of documenting them in various places – excel sheets, tracking tools. A centralized repository for documenting glitches can help in their quicker tracking and better monitoring.
Focus on negative scenarios: Testers should not spend their energies on testing negative test scenarios that are less likely to be used by the end customers. Even though these should be tested during the test cycle, the priority should be set for scenarios that are most likely to be used by the end customers.
Avoid regression testing: Any change made to the application can impact specific areas of it unless regression testing is carried out. Often testers are of the view that regression testing can be avoided as the features or functionalities to be tested had been done earlier. However, any assumption in this regard can be fraught with danger as the changes can cause defects in other areas of the application.
Automate everything: This follows from the ‘dos’ mentioned above where only specific test cases should be automated. The testing team should leave some space for manual testing as automation does not lend itself to every possible scenario. For example, any wrong code in the test script can harm the testing exercise.
Conclusion

With quality forming the centerpiece in ensuring success of any software application, testing or QA cannot be overlooked. In fact, it should be integrated into the SDLC along with development to identify and fix glitches as and when they happen. However, the QA team should religiously follow the dos and don’ts to avoid any negative fallout of testing. The aim, ultimately, should be to deliver the best user experience and achieve ROI.

What makes a holiday wonderful is those family trips that can help you refresh and spend time with your loved ones, while for travel businesses, this means Gold!

With the market flooded by millennials today, customer tours are on the high. Custom tours are those tour packages that are specifically designed for consumers according to their preferences and schedule. That is the sole reason, consumers search for experiences than the actual hotels.

Today, travel businesses around the world are building experiences rather than tours and travel packages. This has led to the acceptance of intelligent technologies like Artificial Intelligence into the design structure of these experiences.

Intelligent Travel Agents(ITA):

Intelligent Travel Agents or ITA is the amalgamation of ML(Machine Learning) and the travel-purchase behavior of the consumers. This is considered as a radical innovation as it ensures consumer engagement through “Smart Services”.

For example, you are at the airport, waiting for your flight and suddenly your entry gate to the flight is changed, you are instantly notified through an ITA regarding the same, without you even leaving your Social Media site.

ITAs uses machine learning techniques to identify the optimum pricing, comfort, and relevance to any particular traveling activity through real-time data analysis and processing through an algorithm trained for the same.

Take an example of the “Virgin Trains Alexa Skill”, launched in 2018, it allowed the passengers to book their tickets directly from Amazon’s Alexa device. This is a prime example of an ITA operating through an intelligent voice bot.

Travel Intelligence-The Personalized Learning:
Artificial Intelligence technologies have a wide acceptance in the business of tours and travels, due to their ability to learn from the real-time data and come up with unique and relevant solutions for the consumers.

There are three different learning models in the Artificial Intelligence that needs to be explored for designing algorithms that can use real-time data to learn specific travel knowledge and use the same to provide more personalized experiences to the consumers.

These three models are:

Supervised Learning
Unsupervised Learning
Reinforcement Learning

Supervised learning uses the data available to train the machine under human supervision. The unsupervised learning model helps machines to learn on their own without guidance or human intervention. But, reinforcement learning targets a particular dataset for training the machine for specific scenarios.

When it comes to travel intelligence use of any of the above machine learning algorithms can be used based on the type of personalization required.

Real-Time Preferences:

In the travel and tourism business, the necessity of real-time preferences have gained traction. Especially in the costing and expenditure aspects of the business, real-time preferences have played a huge part.

Just take an example of online taxi-booking services, transport is an important aspect of any travel and tourism business and this has been revolutionized through the on-demand business models equipped with ML tools that help dedicated app developed through a mobile app development company to predict the total fare of a ride by taking into account all the real-time preferences of the riders.

Self-Optimization:
Once these intelligent machines account for real-time preferences they tend to self-optimize making the very consumer searching for the right experience to be their travel agent. They provide a bouquet of product bundles in the most optimum possible way.

These machines can convey their bundled packages through a mobile interface, voice assistants or other intelligent means. They also impact the consumer’s travel behaviors and purchase behaviors.

Value-Added Services:

A concierge of services is offered by the tours and travel businesses these days like in-flight support, luggage support, lounge access, hotel transfers, medical support during tours, etc. With travel intelligence, these services can be further enhanced.

As these intelligent machines, use real-time preferences as the reference data to train the algorithm for self-optimization and provide enhanced value-added services.

The Role Of Virtual Personal Assistants:

VPAs or Virtual Personal Assistants are the new revolutionary technology that is changing the fundamentals of the purchase behavior of consumers. Current VPAs in the market are Amazon’s Alexa, Apple Home, Google Now, etc. These VPAs understands the natural language voice controls and processes knowledge acquisition.

With the intelligence in their algorithms, these VPAs can be leveraged by the tours and travel businesses to recommend products and activities related to travel. Many enterprises and businesses around the world are already using such technologies for marketing and even sales of the travel business.

Chatbots and Travel Businesses:
Chatbots have become quite useful in the current market scene, as many consumers and travelers always need real-time assistance with their issues during the travel. Chatbots can provide real-time assistance during the travel and also recommend personalized packages to the consumers.

One of the important aspects of intelligent systems is collecting important user’s data, this can be achieved with a series of questionnaires that occur during a chatbot-to-human conversation that almost replicates the natural conversation. Chatbot often excels at natural interactions and collect user’s data that can help the chatbot’s algorithms to learn from them.

Top AI travel companies:
Hopper-Boston & Montreal
Pana-Denver & Colarado
Baarb Inc.-Los Angeles & California
UTrip-Seattle & Washington D.C.
Claire-San Carlos, California
Amtrak-Washington D.C.
Hipmunk-San Francisco, California
Instalocate-Palo Alto, California
Mezi-Sunnyville, California
Signing Off:
The cycle of travel intelligence leading to the integration of real-time preferences and further self-optimization that leads to enhanced value-added services certainly leads to revolutionary change into the travel-distribution and travel-behavior of the travelers. Artificial Intelligence has truly transformed the travel business into something more than a regular business activity.

There are concerns around the data protection and regulation in the adoption of Artificial Intelligence and machine learning technologies that can expose the user’s data to data risks and identity thefts.

But, many regulatory bodies around the world are in the process of developing data regulations for data collection, mining, and analysis in the most secure manner to ensure proper adoption of such intelligent technologies with security and safety. So, if you are into a travel and tourism business, just go for an AI-based model to rip the fruits of intelligent labor!