Qapitol QA


Quality Engineering

What is QE 

Quality engineering involves conducting quality checks throughout the software development cycle instead of doing them as a siloed activity at the end of the development. This ensures early detection of defects, lesser rework, deeper test coverage, better quality, and faster releases since dev and testing happen simultaneously. It consists of a set of guidelines, tools, frameworks, and methodologies to validate the software’s performance, security, and quality at every stage of the development. Integrating software testing within the dev cycle as part of quality engineering also enables a more productive utilization of resources and seamless collaboration among stakeholders.

Quality Engineering v/s Software testing.

While both Software Testing and Quality Engineering focus on augmenting the software build quality, their focus and approach vary greatly.

Software Testing validates the quality of the product after it has been developed. However, Quality Engineering aims to improve not just the product quality but also the efficiency of the testing organization, the extent of automation employed in the testing process, delivery timelines, and overall resource utilization.

In software testing, manual testers conduct functional and non-functional tests on the product to ascertain its quality and performance, followed by a detailed report. It is often an activity independent of product development. Hence, both teams have their own set of tools and frameworks to execute their designated roles. The defects and gaps highlighted are then reported to the dev, who does the required rework to ensure these are fixed in time. While in theory, this approach looks neat, in today’s world of faster releases at an optimum cost of quality, such a siloed one-after-the-other methodology derails delivery timelines. It also spirals up the estimated cost of quality.

Quality engineering is a more holistic approach to developing quality software at an optimized cost. It aims to instill quality-centric thinking into the processes and people involved in the software development cycle. Here, quality-centric thinking should not be mistaken as striving for perfection but as an ever-evolving philosophy of product quality that considers end-user preferences, delivery timelines, and budgetary limits to employ process, time, and cost efficiencies to get the desired output within the stipulated time.

Business Benefits of Quality Engineering

Improved cost of quality due to lesser rework, automation of repetitive testing processes, cross-functional sharing of resources, and minimization of procedural wastage.

Faster bug detection due to implementation of automation for improved accuracy and consistency in bug detection at every stage of STLC

More efficient utilization of testing talent since most repetitive tests are automated, ensuring testers have bandwidth for higher-order tasks that use their creativity and analytical approach. This also improves their morale and productivity at work.

Enhanced cooperation between dev and testing since it requires both teams to work simultaneously on building a great quality product within a stipulated time and using resources in an optimized manner.

Builds a reinforcing cycle of continuous improvement as the constant evaluation of the process and product generates a lot of valuable insights that stakeholders use to augment the testing organization further and raise the bar for higher quality.

Quality Assurance

Quality Assurance is all about verifying if the software is confirmed to the specified quality standards and requirements. It consists of activities focused on testing and reporting. Historically, testing was mainly a manual task, but as technology progressed and products evolved, the need for automation became even more prominent. Automation now plays a significant role in improving the accuracy and consistency of testing activities, augmenting productivity, accelerating release velocity, and promoting standardization across testing organizations.

As technology gets more embedded in everyone’s life and products become software-centric, Digital Assurance is now replacing Quality Assurance.

Digital Assurance involves a complete set of value ingredients to deliver an unparalleled experience to your end customers. It’s more of a philosophy that consists of guidelines, frameworks, strategies, tools, and expertise needed to smoothly transition from the current state to the desired quality-centric state.

Role of Quality Assurance in Software Development

  1. Quality assurance helps you to optimize resources by reducing procedural inefficiencies, augmenting productivity with appropriate use of automation, and promoting consistency through standardization.
  2. Quality assurance helps you to deliver high-quality products by building robust processes that ensure deeper coverage, prevent defect detection, and provide comprehensive visibility into the STLC.
  3. Quality assurance also enables seamless knowledge transfer by designing processes and guidelines that ensure detailed documentation of every aspect of STLC. Such comprehensive documentation ensures that newer team members can grasp the intricacies with minimal onboarding effort.
  4. Quality assurance enhances cross-functional collaboration among stakeholders like developers, testers, and product owners to nurture commitment toward improving software quality by sharing, revising, and recommending best practices.

Is Quality Assurance the same as Quality Control

  1. Quality assurance aims to refine the testing processes, approach, and the entire quality philosophy of the organization to realize the desired quality output of the software products. In contrast, Quality control aims to check and identify the defects of the software product being made before it gets shipped out to the end user.
  2. Quality assurance focuses on the entire testing setup. It aims to refine it further, whereas quality control focuses on the software product developed to validate if it meets the required quality goals.
  3. Quality assurance is more of prevention, whereas quality control involves only the detection.

Adopting quality assurance practices is about more than just improving your testing capabilities. It’s about building a foundation for long-term success, sustained customer satisfaction, and scalable growth. Organizations that adopt QA demonstrate a commitment to excellence, innovation, and delivering real value to their end customers.

Agile Quality Engineering

Fret not! Agile Quality Engineering is not a new term in the QE universe. We emphasize the need for an agile mindset while implementing quality engineering principles for on-time, high-quality releases.

What it means to combine Agile and QE

Agile Quality Engineering aims to integrate quality engineering practices seamlessly within an Agile software development framework to ensure on-time, high-quality software releases continuously and iteratively. It emphasizes the collaboration between development and quality assurance teams to
– Enable an appropriate shift-left approach,
– Introduce just the right amount of automation to power up speed and consistency
– Conduct frictionless continuous testing for shortening feedback loops with the dev teams

Why should you combine Agile and Quality Engineering

Agile enables the development and delivery of software products in an iterative manner. This approach is suitable when requirements constantly evolve, which calls for tighter collaboration, accelerated testing, and shorter feedback loops to adapt swiftly to the changes. That’s why agile requires embedded quality engineering to ensure its success and sustenance.

When applied in an agile setup, quality engineering practices ensure that testing and QA are in sync with development pace and consistency, enabling it to deliver on the quality promise within the stipulated time. No team is working in silos or is hindered by bureaucracy. Resources are shared, workflows are standardized, and visibility is at an all-time high for all stakeholders to monitor progress and suggest improvements.

Quality engineering without an agile mindset tends to slow down development, focusing solely on improving the quality of the product and processes. In contrast, Agile development without Quality engineering leads to faster shipping of poor-quality releases. Hence, the much-needed integration!

Challenges you will need to deal with when you are combining QE and Agile

Integrating quality engineering practices into Agile development poses specific challenges.

1. The Mindset Switch – Transitioning from traditional development practices to Agile Quality Engineering requires a cultural shift. Developers and testers need to collaborate better, communicate more effectively, test more often, report more exhaustively, document more comprehensively, and act swiftly. All of them require a smooth transition from previous approaches and workflows to

2. Implementing and Maintaining Automation: While test automation is a crucial aspect of Agile Quality Engineering, setting up and maintaining automated test suites can be time-consuming and require specialized skills.

3. Keeping up with Continuous Feedback: Agile methodologies rely on continuous feedback from stakeholders, which can lead to frequent changes in requirements. Adapting to these changes while maintaining quality is challenging and time-consuming.

4. Getting resource allocation right: Balancing development and testing resources can be tricky. Teams must allocate enough resources for testing while not impeding the development process. Automation and low code technologies play the role of enablers here by taking over mundane tasks.

5. Bridging the Skill Gap: Teams may need more skills for effective test automation, continuous integration, and other Agile Quality Engineering practices. Training and skill development are often required. However, often, these initiatives need more returns. A more resourceful alternative is to collaborate with the right partners who not only have the required expertise but also

Addressing these challenges requires effective communication, collaboration, and a commitment to continuous improvement. Agile teams should be willing to adapt their processes and practices based on the unique needs of their project and organization.

Continuous Testing

Continuous Testing is a software testing approach that enables automated and continuous execution of tests throughout the entire development cycle to validate the software changes thoroughly as soon as they are integrated into the codebase. It ensures faster development cycles, higher software quality, and improved collaboration between development and testing teams, enabling teams to deliver faster and with reliable software quality.

Continuous testing and Continuous Delivery – Are they synonyms?

Continuous Testing and continuous delivery, often used together, are two critical components of a successful and sustainable DevOps methodology. However, they both mean different things. While Continuous Testing is about thoroughly executing tests to validate the code changes, continuous development is about automating the build and deployment of software changes. It enables teams to expedite the release of reliable software into production or staging environments. This is enabled with the integration of continuous Testing. Hence, they are used together.

Continuous Testing feeds into continuous delivery by validating all the automated builds for defects and ensuring only high-quality builds are pushed to the deployment stage.

Key Components of Continuous Testing

  1. Test Automation – Test automation is one of the critical pillars of continuous Testing since the continuous execution of tests to validate the changes are materialized only through automation. Test automation ensures that the process is consistent, accurate, and fast. These tests cover various aspects of the software’s functionality, from unit tests to end-to-end tests, thereby providing a deeper test coverage when compared to manual Testing.

  2. Shorter feedback loops – Since the tests are executing continuously, the feedback loops are shorter and regular. This enables dev teams to fix the defects early in the development cycle. However, the key enabler for this is the level of collaboration that testing and dev teams showcase. A tighter collaboration ensures the free flow of information and seamless knowledge sharing, enabling the product teams to deliver faster and with high quality. Whereas suppose the teams work in silos and under bureaucratic layers. In that case, the pace of continuous Testing takes a sharp hit, thereby stalling the progress of the overall delivery.

  3. Tighter collaboration between dev and testing teams – As highlighted above, a seamless collaboration enables teams to exchange and act upon feedback faster and also results in delivering high-quality releases. Another benefit of excellent test-dev cooperation is more efficient usage of resources, thereby producing cost efficiencies.

    This directly and positively impacts the cost of quality for the organization. Also, this leads to further refinement of the organization’s testing and quality engineering approaches when cross-functional teams share their learnings and insights for everyone else to act upon.

  4. Shift-left approach – In contrast to the waterfall approach, continuous Testing enables a shift-left approach wherein the Testing is done early in the development cycle – thereby reducing the cost of rework, ensuring defects are fixed early on, and minimal defect leakage into production.

Continuous Testing helps organizations deliver high-quality software more quickly, efficiently, and reliably, improving customer experience scores, building loyalty, and augmenting revenue.

API Testing

API testing refers to the testing and validating the functional and non-functional aspects of the individual APIs and the interaction between APIs. These tests can be conducted manually or through automation. But considering the volume of APIs being built and released by development teams, it’s advisable to opt for automated API testing to ensure higher accuracy and consistency of results at an expedited pace.

API testing broadly validates the following:

  • Functionality, performance, and scalability
  • Ability to handle exceptions
    Responses to different input values
  • Security loopholes
  • Load handling capacity
  • Interaction among different APIs

Why So Much Fuss Around API Testing

While digital transformation was happening at firms of various sizes at different speeds, COVID-19 just accelerated its pace. Companies had overnight to rejig their operating models and value delivery approaches to become digital first. One of the critical elements of a viable digital-first strategy is implementing seamless, functional, and secure APIs.

Though APIs represent just a piece of software in theory, they hold the doors to invaluable amounts of customer data, which, if left vulnerable, might become the breeding ground for hackers and lead to irreparable loss of business and consumer trust. Also, the advent of APIs opened the doors for newer value delivery opportunities that existing companies could employ to serve their customers better and improve topline.

Hence, a thorough testing of APIs is critical before they are sent to production.

Tools Available for API Testing

While several tools are available and popular today for API automation, do they meet the ever-evolving complexity of APIs and the dynamic needs of organizations today?
Most of these tools do not provide deep coverage of functional aspects, lack appropriate collaborative features to augment productivity, provide limited visibility into the API lifecycle, and fail to capture sufficient insights and data for faster knowledge retention and sharing – to name a few. However, these are tertiary challenges that do not immediately threaten the quality of APIs. But each of these struggles has consequences regarding cost and time inefficiencies, missing out on innovation and process improvement opportunities, and introducing friction in the agile/DevOps environment. Hence, we need technologies that resolve them and improvise API testing further.

Low code/no code presents a promising opportunity. Some of the significant advantages of this technology include easy accessibility to non-technical users, low maintenance, improved reusability of templates and other collaborative features, extensive reporting dashboards for comprehensive visibility, faster implementation, and smoother knowledge transfer across teams.

APIs are already a game changer in the software world, thanks to their ability to support microservices architecture, improve user experience, accelerate development, and introduce newer value propositions. Hence, API testing is one of the key strategic initiatives of every testing organization. However, the challenge lies in designing and implementing a robust and reliable testing approach that augments the speed and quality of APIs by seamlessly integrating with the current workflows without the need for a steep learning curve for adopting teams. Only then would their testing bear effective fruits and align with the intended business outcomes.

Accessibility Testing

Accessibility testing is performed on software applications to validate their usability for differently abled users. Any software application aims to perform flawlessly and be accessible to users with different needs. Hence, through this test, QA and developers determine if the application is accessible to all users, including those with hearing/visual impairment or any other form of disability.

Accessibility testing accrues the following benefits for your product and brand:

  1. Promotes exhaustive testing of the application from your users’ perspective. In the world of software products that aim to cater to different types of users, if your product stands out and can be helpful for one and all, it will ensure a higher opt-in rate and favorable mind space among your users. Not only will it help you gain a more extensive user base, but it will also help to expand territories beyond your home turf. This can lead to increased market share and revenue opportunities.
  2. Promotes better legal compliance in geographies with stricter accessibility rules in place. As per a WHO report, around 15% of the world’s population has some form of physical disability. Hence, different countries follow different sets of guidelines and regulations around accessibility, which makes it critical for a software product to undergo accessibility testing to validate if it complies with local regulations. Whether manual or automated, it’s imperative to include this testing to ensure you abide by the legal compliance of the country where you aim to market your product and have this significant part of the user population as in the target group.
  3. Promotes equity and inclusivity. As technology becomes an essential part of our everyday lives, it makes more sense to build products that cater to the needs of all types of users. Just like incorporating ESG initiatives ensures you do your bit for a greener tomorrow, embracing accessibility testing enables you to support inclusivity and contribute towards building a more equitable digital environment for all.

Accessibility testing should be a priority for every industry that relies on software products or digital platforms to provide customer value. The customers could be internal or external, businesses or retail users, but as long as your digital offering is going to be used by a human, it makes complete sense to get it validated by its accessibility using this test. It ensures that your software is accessible to all users, irrespective of their needs. Here, one might de-prioritize it for the sake of speedier releases. Still, the long-term consequences of leaving a sizable portion of your target audience unattended will speak volumes about your organization’s inclusive and ethical priorities.
Whether or not to automate and how much to automate in accessibility testing should be one of the key priorities to be discussed before your QA team embarks on this journey. The decision may not be a straightforward one and require deep-dive thinking and brainstorming across the following points:

– Exhaustive user profiles
– Compliance needs
– Time and cost constraints
– List of pre-defined scenarios to be automated

This is because accessibility testing is all about navigating software through the eyes of a user with disabilities. While specific scenarios and test scripts could be automated, that may be a partial list. Despite automation, there could still be a lot of room left for a manual tester to explore and identify newer accessibility scenarios to be validated.

Agile Testing

Agile testing refers to the software testing methodology that integrates the best practices of agile software development methodology.

Agile software development methodology takes an iterative approach to developing software by breaking it down into smaller modules (user stories, tasks, requirements). This results in faster releases with cleaner quality by collaborating closely with all stakeholders and incorporating changes/feedback with minimal delays. It shortens the development time, saves costs, and promotes building products that align closely with customer preferences (read better quality).

In agile testing, application testing is done simultaneously as the features are being developed and not as a standalone activity after development. This shifts the testing phase early in the development cycle, often called “shift-left testing.” Since features are tested as they are being developed, bugs and defects are highlighted early on, thereby ensuring that they are fixed by developers sooner rather than later. This early bug detection and fixing saves much time, cost, and effort on rework compared to doing all this after a feature has been fully developed and ready to be deployed.

However, while all of this looks neat and manageable in theory, sustainable and seamless agile testing requires teams to work closely with each other, share feedback in real-time, provide ample visibility on the STLC, and focus more on executing and not just documenting.

Agile Testing reduces the overall software deployment time by pre-poning the testing phase. This substantially saves development time, cost and effort required for rework since the bugs are detected when the feature is still under development.

However, there is an indirect impact of this on the morale of the product teams. Faster and earlier bug detection ensures a better utilization of dev and testing talent, boosting their morale.

Imagine a dev team that has taken X amount of time to build a feature. When the QA team tests the fully developed feature, they send their feedback and bug list to the dev team. At this point, the dev team will have to sit through the entire list of bugs, identify the relevant code, fix it, and update the feature with all the changes.

So much rework after a feature has been fully developed impacts the team’s morale negatively since their time and skill could have been used for other higher intelligence tasks. Compare this with agile testing, wherein dev teams receive real-time feedback about bugs while working on the feature. Locating and fixing these bugs becomes easier when you are already involved in building a feature rather than when you are done developing the feature to the T.

The most critical components of agile testing include

  • A robust and proven testing framework that considers business requirements, product goals, organizational QA maturity, and available technologies to deliver the program objectives in a simple and structured manner yet flexible enough to adapt to ongoing changes
  • Agile testing experts with the required experience to iron out all hindrances during implementation, establish the culture necessary to make it sustainable, and build processes that foster innovation and collaboration in the testing organization
  • Just the right amount of automation to augment testing efforts’ efficiency, accuracy, consistency, and reproducibility
  • Supporting tools and tech stack that will help implement agile testing successfully. This is especially true for legacy systems that may have to undergo digital and cultural transformations to integrate agile testing as part of their operations.
  • Ample visibility for all stakeholders to track progress, track insights for further improvement of the testing approach, and share knowledge among cross-functional teams.

Agile testing promotes collaboration, adaptability, early issue detection, and a customer-centric approach. Teams can deliver higher-quality software more efficiently, with a focus on continuous improvement, cost optimization and customer satisfaction. But all of these are the end goals! The means to achieve these is what determines failures from successes in agile testing adoption rate.

Automated Testing

When you automate test execution with the help of tools and tech stack, it’s called Automated Testing. Several testing types require repeated execution of predefined test cases multiple times. Doing them manually results in losing precious testing resources, inflates the project cost, and slows delivery schedules. Conducting the same test repeatedly also makes it mundane to the extent of lowering your tester’s morale, increasing the chance of mistakes and inaccuracies in test execution or reporting.

Automated software testing can execute tests many times faster than manual testing. Since it’s a tool-centric testing mode, it brings greater accuracy and consistency in results. The test scripts are written and fed into the system by manual testers. However, with the advent of low code/no code, test scripting is no longer limited to technical users. Business users can write and execute tests using a combination of low/no code testing frameworks.

The benefits of automated testing include

  • Shortening of software release cycles
  • Better use of testing talent
  • Shorter feedback loops lead to fewer bugs in production
  • Better optimization of project cost and resources

Since software development companies now aim for releases within a matter of weeks, automated testing appears as the much-required enabler. Should you aim for 100 percent testing automation as a testing organization? The answer depends on your product goals, business requirements, and end user’s perspective.

Tests like regression testing, load testing, etc are inherently repetitive. Usually, they are the first ones to be automated. But for tests that require your testing specialist to use innovation, an out-of-the-box approach, and your end-user’s perspective – are usually tricky to automate. Because they are more exploratory and involve unpredictability in execution/results, they cannot and should not be automated. But that still leaves much room for test automation to be implemented to improve the accuracy and efficiency of your testing cycle.

Now that we are clear on what to/not to automate, it’s essential to clarify a few more things when you are about to implement automated testing in your STLC

  • Test automation should not be a random on-the-go exercise during the testing lifecycle. That way, it adds to the inefficiency by unnecessarily creating more significant gaps, increasing project costs, and promoting non-adherence in its usage
  • You should always opt for one framework to accommodate all business requirements and product goals. Such a framework must be robust, easy to implement, scale, and flexible to changes. This practice promotes efficiency, a shorter learning curve, and cost savings.
  • You should have experienced domain experts implementing automated testing, not just test automation experts. Building and implementing test automation that snug-fits your requirements requires your team to have an in-depth domain understanding, a fair amount of technological experience, seasoned expertise in test automation, and a reliable ability to navigate seamlessly in business environments like yours. Though never documented in any test automation tool, these nuances ultimately make or break a test automation implementation.
  • Always stress on maintaining complete visibility for all stakeholders at every stage of STLC. This is done by keeping exhaustive, easy-to-use, and shared documentation. Such thorough visibility promotes transparency and accountability in STLC. It fosters newer insights and ideas for further improvement of the testing organization.
  • Create relevant metrics to appropriately track test automation’s progress and ROI. This will not only help you justify the cost of this initiative but also model and improve your overall cost of quality

Whether you are a digital native aiming to provide superior value or a legacy enterprise aiming to become digital first, automated testing can help achieve your goals faster and in a consistent manner. However, it requires careful planning, thorough implementation and stringent tracking to make it sustainable and produce desired results.

Data Driven Testing

Data-driven testing is a software testing methodology that separates test scripts and input data. This method allows the use of a single test script for validating multiple data sets. Data sets are stored in separate files/databases like ADO objects, ODBC sources, CSV files, etc., rather than being hard-coded into scripts.

This de-linking of test script and test data has several tactical and business benefits:

  • It helps to reduce test script redundancies since a single script can be used with multiple data sets
  • It brings greater accuracy and consistency to your testing process
  • It enables smooth scalability of your testing organization
  • Updating and maintaining the test cases becomes easy since the values are not hard-coded
  • Tests can be executed faster, and the same set of scripts can be reused and shared across cross-functional teams
  • It promotes deeper test coverage since multiple input data sets can be tested without increasing the workload of the testing team
  • It enables teams to share testing resources seamlessly and promote better collaboration
  • It also leads to significant cost savings for testing organization

With so many benefits, it could be tempting to conclude that data-driven testing should be the be-all for software testing. However, that is not the case. Data-driven testing improves the efficiency of the testing process. However, it is only a replacement for some kinds of test cases.

Data-driven testing should be employed in scenarios of

  • Multiple input data values for the same script
  • Test cases that involve frequent updates/changes
  • Test cases that need to be executed multiple times, like regression testing

Other than the above scenarios, data-driven testing has limited scope. So, if your team is

  • Executing tests with no pre-defined outcomes like exploratory testing
  • Executing tests that are conducted once in a while that are rare in nature,
  • Executing tests in which results are fairly complex

Employing data-driven testing might not be the most efficient choice!

When using data-driven testing, you should consider several key factors to ensure its successful implementation.

  1. It begins with a well-defined strategy, which is essential. This strategy should include clear objectives, such as the types of tests to be conducted and the expected outcomes. It’s crucial to identify the data sources and establish processes for data extraction, transformation, and loading (ETL) to ensure the data’s integrity and consistency.
  2. Appropriate investment in scalable automation tools and frameworks. You must invest in robust automation frameworks and tools capable of handling data-driven testing effectively. These tools should support data parameterization, data management, and reporting. Test data should be carefully curated, covering a wide range of scenarios, including boundary and edge cases. Data maintenance should also be seamless and a no-brainer on these frameworks to enable sustainable adoption.
  3. Go all out on data security. You should prioritize data security and privacy, especially when dealing with sensitive or personal data. Compliance with relevant regulations, such as GDPR or HIPAA, is essential. Data masking and anonymization techniques may be necessary to protect sensitive information during testing, storage, and extraction.
  4. Monitoring progress: Lastly, continuous monitoring and reporting are critical. It would be best to establish metrics and KPIs to measure the effectiveness of data-driven testing and make improvements as needed. Regular reviews and collaboration between testing and development teams can ensure that data-driven testing aligns with project goals and contributes to the software’s overall quality.


Debugging is the process of locating and fixing bugs/errors in a software program. It can either be done manually or through automation. Debugging software applications help improve code quality and application performance and prevent unexpected crashes and timeout errors.

When debugging software, programmers look for the following types of bugs:

  • Syntax errors
  • Typos
  • Errors in logic
  • Implementation errors

Debugging typically involves the following steps:

  1. Issue Identification – The first step involves locating the issue/problem in the software by understanding the symptoms. The symptoms typically include unexpected software behavior or error messages. Programmers usually look at the signs or check error logs for further clarity. The details of the symptoms give enough clues about the hidden bugs- their location in the source code and nature.
  2. Issue Reproduction – This step often overlaps with the first one when the symptoms do not clearly indicate the error location. Programmers replicate the symptoms by following the steps that produce them to backtrack the code that requires fixing.
  3. Root Cause Isolation and Analysis – This step involves isolating the root cause (the code with the bug) causing the symptoms. This often results from reproducing a sign or a problem, which leads the programmer to the source code that hides the bug/error.
  4. Code Modification and Fixing – After identifying the code, programmers either manually modify it to fix the error or use automated tools to debug it.
  5. Fix Validation – In this step, the new code is tested to check if the bug has been fixed and the code works as expected.
  6. Regression Testing – Once the above test is done, it is followed by regression testing on the whole software to check whether the code changes have affected any other part of the software.
  7. Updated code release – Once the programmer is confident in updated code and software quality, it’s released in production.
  8. Documentation – While this is mentioned towards the end, in reality, programmers document every step in the process in a detailed manner for future reference. Hence, debugging documentation should be considered an ongoing part of the process, not a task kept for the end.

Debugging is an iterative process since developers may need to repeat the process multiple times to address all software errors effectively. Effective debugging skills are essential for software developers and testers to create high-quality and reliable software.

Debugging is not the same as software testing. The critical difference between the two is the objective of running both processes on a software application. While software testing detects errors/bugs in software, fixing them is when debugging comes into the picture. Software testing is primarily done by QA/software testers and occasionally by developers, but debugging is done mainly by developers/programmers who have written the code.

Debugging plays a vital role in software development. Effective debugging improves code quality, reliability, user satisfaction, and software success. It is an ongoing practice that should be integrated into the software development life cycle to produce high-quality and dependable applications.


The Internet of Things (IoT) pertains to a network of physical objects, referred to as “things,” equipped with sensors, software, and related technologies. These objects seamlessly connect and share data with other devices and systems via the internet without human intervention. This technological framework facilitates the gathering and dissemination of data across a wide array of devices, offering prospects for improved efficiency and automation within systems.

An everyday example of IoT is collecting and sharing traffic data from your vehicle using your mobile phone as a sensor and via apps like Google Maps or Waze. So here, even though your mobile phone is not an IoT device (since it requires human interaction to operate), Google Maps is an IoT application that uses the sensors in your phone to collect and share traffic data with other users in its network.

Enablers of IoT

  1. Low-cost, low-power sensor technology ensures the production of cost-effective sensors embedded in devices that collect and share data across the IoT network.
  2. Cloud Computing enables seamless storing and sharing of massive amounts of data collected across different devices in IoT.
  3. Seamless connectivity ensures that data collection and transfer is smooth and in real-time.
  4. Machine learning and analytics help providers generate valuable insights from the data collected across IoT devices. These insights help to refine the value delivered through this technology.
  5. Conversational AI enables computing devices to simulate real conversations with end users. Examples are Alexa and SIRI.

IoT Technology v/s IoT Applications v/s IoT Devices

IoT Technology refers to the technology that enables machine-to-machine (M2M) communication, data collection and sharing without any human interaction.

IoT applications are software/services that integrate data received from various IoT devices, analyze them for insights, and then share these insights with the IoT devices to respond.
Example – Google Maps, Waze

IoT devices are physical devices with embedded sensors and computing capabilities that collect and share data across other IoT devices.

The global consumer IoT market was valued at $220.5bn in 2022 and is expected to grow at a CAGR of 12.7% from 2023 to 2030. Much of this increase is credited to the growing popularity of smart devices in the retail segment and consumers’ increased need for convenience.

IoT continues to grow as the enabling technologies evolve and become more integrated into various aspects of our lives. Here are some key areas where IoT has significant potential:

Increased Efficiency: IoT can enhance efficiency in various sectors, including manufacturing, agriculture and logistics. Organizations can automate processes, reduce waste, and optimize resource utilization by collecting and analyzing data from sensors and devices. One of the key benefits would be significant long-term cost savings, especially in industries like manufacturing and transportation.

Enhanced Quality of Life: In smart cities and smart homes, IoT can improve the quality of life by making urban environments more sustainable, reducing traffic congestion, enhancing security, and augmenting energy efficiency.

Healthcare Advancements: IoT devices can enable remote patient monitoring, early disease detection and personalized treatment plans, thus leading to better health outcomes, expanded coverage and lower healthcare costs.

Safety and Security: IoT, through applications like smart surveillance, emergency response systems, and asset tracking, can help prevent accidents and respond more effectively to emergencies.

Data-Driven Decision Making: IoT generates vast amount of data for stakeholders to extract valuable insights that can strengthen decision-making and improve strategic planning.

Ever-increasing Convenience: IoT-powered devices and services, such as voice assistants, smart appliances, and wearable technology, offer convenience and personalization, enhancing the consumer experience forever.

Bridging the Digital Divide: By extending connectivity to remote and underserved areas, IoT can help industries and governments bring the benefits of connectivity under the reach of a greater section of society.

Transportation and Mobility: IoT is a crucial enabler for autonomous vehicles and smart transportation systems, thereby reducing traffic congestion, improving road safety, and enhancing the overall transportation experience.

While IoT offers tremendous potential, it also presents challenges related to data privacy, security, interoperability, and scalability. Addressing these challenges will be essential to fully realizing the benefits of IoT while ensuring the safety and privacy of individuals and organizations.

Low Code

Low-code process automation is automation with minimal manual coding. It empowers users to design, automate, and optimize workflows through a visual interface, drastically reducing complex programming needs. This technology enables business users (read non-technical users) to swiftly adapt to agile demands by efficiently automating repetitive tasks and orchestrating processes across systems.

By simplifying development, low-code process automation enhances productivity, accelerates application delivery and fosters collaboration between IT and business users. It’s a versatile tool for achieving operational efficiency and agility in today’s fast-paced business environment.

Low code addresses several critical operational workflow challenges.

  • It expedites application development by minimizing the need for intricate coding, reducing time-to-market for new solutions. It also reduces dependency on IT and reduces their workload.
  • It enhances operational efficiency by automating repetitive tasks, reducing errors, and optimizing workflows. This is exemplified in supply chain management, where automated order processing minimizes errors and expedites deliveries.
  • It augments collaboration between IT and business units, ensuring that technology aligns with strategic goals.
  • It enables organizations to swiftly adapt to changing market conditions, as seen in finance, where it streamlines loan approval processes, meeting customer demands more rapidly.

Low code is effective for workflows demanding rapid development and frequent changes, such as customer-facing apps or CRM systems. Financial institutions also benefit from low code in customer onboarding by automating the redundant yet necessary steps, thereby augmenting onboarding speed and ease.

However, low code isn’t advisable in complex, mission-critical systems, like aerospace systems, apps related to national security, life-saving medical devices, and high-frequency trading systems due to inherent safety and reliability risks. Also, applications requiring intricate algorithms, like scientific simulations, demand traditional coding for precise control and accuracy.

Low code offers immense value where agility is paramount, but a traditional coding approach remains a more dependable option for complex or algorithm-intensive systems.

Low code comes with its limitations. The popular low-code tools available in the market often need more flexibility for more complicated requirements. Additionally, as low code often abstracts technical details, fine-tuning performance can be challenging in resource-intensive applications.

Also, not all legacy systems seamlessly integrate, possibly requiring custom coding for compatibility. But the good news is that solutions are available now that enable users to customize app development even in low code. These solutions are a positive step toward overcoming the challenges presented by the current low-code tools, making this technology even more accessible and valuable in app development and process automation.

As technology advances, we may see low code capable of catering to scenarios that are out of scope currently. It is already en route to becoming adept at automating routine tasks and critical decision-making processes, enhancing its efficiency and value further. Security features will become even more robust, addressing concerns comprehensively. Also, integrating IoT and data analytics will open new frontiers, enabling predictive and data-driven applications. The future of low code is about empowering users to build robust, custom solutions with ease and speed.

Mobile Testing

Mobile testing verifies mobile apps’ functionality and user experience on different devices and varying network availability. It’s needed because mobile devices vary in size, performance and software, so apps may behave differently, leading to a non-uniform user experience.

Some 7.33 bn people today own a smart or a feature phone (representing 90.97% of the world population). The latest research suggests that people spend 90% of their mobile time on apps. This indicates the importance of mobile apps in your consumer’s life. Hence, it is critical to ensure that the mobile experience of your apps is seamless, secure, and fast, and that’s what is verified by mobile testing. Mobile testing ensures your apps and smartphones are safe, reliable, and user-friendly.

Mobile application testing helps find and fix bugs, improve app quality and performance, and create a safe haven for user data. Mobile testing covers areas like functionality, performance, security, and usability. It contributes to reliable and user-friendly app functionality, which is vital in today’s mobile-driven consumerism.

Mobile testing can be implemented via manual as well as automated ways. Both testing methodologies are crucial in ensuring app quality, but automated testing offers distinct advantages. Automated testing is faster and more efficient for repetitive tasks, enhancing productivity, aiding the consistency of testing efforts and reducing testing time. It also provides broader device coverage, detecting issues across various platforms consistently.

Automated tests are reusable, reducing the effort required for regression testing as the requirements evolve. While manual testing allows for exploratory and usability testing, automated testing excels in regression, performance, and load testing. Combining both approaches optimizes mobile testing, ensuring a robust, user-friendly, and efficient app development process.

Mobile testing comprises various critical tests:

  • Functional testing checks if the app’s features work as intended, ensuring a smooth user experience.
  • Performance testing assesses speed and responsiveness, which are vital for user satisfaction.
  • Compatibility testing ensures the app functions well on different devices and operating systems, expanding its reach.
  • Security testing identifies vulnerabilities, safeguarding user data.
  • Usability testing evaluates user-friendliness and overall experience, preventing user frustration.
  • Regression testing ensures that updates don’t introduce new problems.
  • Localization and accessibility testing adapt the app to a diverse set of users.
  • Network testing assesses performance under different network conditions.

All these tests are indispensable, guaranteeing a reliable, high-quality mobile app that meets user expectations and industry standards.

Effective mobile app testing requires testers to have technical expertise, a clear understanding of the domain, and an inbuilt empathy for the end users. While in-house teams are valuable, partnering with experienced external QA specialists offers distinct advantages. The experience of working with different industries catering to different audiences is an inherent advantage that seasoned QE partners have with them. They bring in domain and technical expertise and can conform to the end user’s perspective more inclusively. All these insights help build and test for diverse testing scenarios and minimize associated risks. Failing to get mobile testing right can lead to negative user experiences, financial losses, and long-term reputation damage. Collaborating with skilled external partners ensures comprehensive, unbiased testing, guaranteeing a robust, user-friendly app ready for the long term.

Performance Testing

Performance testing is critical to software quality assurance, ensuring that an application performs well under various conditions. It evaluates your app’s speed, responsiveness and stability and identifies bugs and errors.

This type of testing comprises critical tests like load testing, stress testing, endurance testing, spike testing, volume testing and scalability testing. Load testing assesses how an app behaves under expected load levels, while stress testing pushes it beyond those levels to uncover vulnerabilities and weaknesses. Scalability testing determines if the app can handle increased load as user numbers grow.

Performance testing is indispensable because slow or unreliable applications can lead to subpar user experience, high drop-off rate, loss of revenue, and poor customer loyalty and trust. By thoroughly testing an application’s performance, businesses can ensure it meets user expectations, functions reliably, and can handle varying user loads, ultimately augmenting its success among users.

But testing an app’s performance, as critical as it may sound, is a challenging workflow to implement.
Also, the consequences of false performance testing results (arising due to incorrect handling of the execution) are often long-term and irrecoverable.

  • Resource limitations often hinder the accurate simulation of real-world conditions, leading to scalability issues.
  • Managing large datasets and maintaining consistent data for testing can be complex, especially when the requirements scale.
  • Predicting performance under varying loads, considering factors like mobile device fragmentation and third-party dependencies, requires careful planning and accurate predictive analysis.
  • Variability in test data and the intricate process of monitoring and analyzing performance data can also pose challenges in consistency, too many false test results, and gaps in reporting.

Addressing these issues demands technical expertise, a thorough domain understanding, and working knowledge of technology, tools, and coding clarity. Finding such a combination in-house is often a challenge faced by product teams across enterprises and high-growth firms.

This is where experienced QE partners are of immense help. Their technical expertise and decades of experience working with diverse industries and product requirements give them an undue advantage in knowledge, experience, and wisdom. They can easily navigate through the interconnected worlds of product requirements, end-user perspectives, and constantly evolving technologies to shift gears wherever needed to build and implement effective performance testing practices at client organizations of all sizes. Successful performance testing ensures applications can handle user demands efficiently, offering a seamless user experience and averting potential financial and reputational risks.

There is also an updated version of performance testing practiced by agile organizations, which goes by the name of continuous performance testing. Continuous performance testing is conducted throughout the software development cycle, whereas traditional performance testing is performed towards the end of the development and just once.

Continuous testing helps to identify and address issues early, reducing the likelihood of costly fixes later. It promotes a proactive approach to performance optimization, enhancing the user experience from the outset. Continuous performance testing supports agile development, accelerates time-to-market, and fosters a culture of performance awareness by providing immediate feedback on code changes and their impact on performance. In today’s fast-paced digital landscape, continuous performance testing is beneficial and essential for delivering high-performing, competitive applications.

POS Testing

Point of Sale testing is an essential aspect of software testing for retail and hospitality industries. It ensures that payment terminals (hardware and devices), software and peripherals function accurately and securely during transactions.

POS testing validates functionalities like transaction processing, payment methods, receipt generation and integration with the more extensive enterprise architecture. Testers ensure the system’s reliability under diverse conditions by simulating real-world scenarios, like network failures or high transaction volumes — additionally, security testing in POS checks for vulnerabilities, safeguarding customer data. For instance, testing EMV chip card transactions verifies the card reader’s functionality and data encryption, which is crucial in modern POS systems. Effective POS testing guarantees a seamless, secure, and efficient customer checkout experience, fostering trust and customer satisfaction.

POS testing involves a systematic approach to validate the entire payment ecosystem. It comprises

  • Functional tests to ensure accurate transaction processing and receipt generation.
  • Security tests focus on data encryption and protecting customer information from potential threats.
  • Compatibility tests to assess the integration with various payment methods like credit cards and mobile payments.
  • Scalability tests to evaluate the system’s performance under unexpected and extreme scenarios like high transaction volumes, peak season traffic, etc.
  • Regression tests to validate system performance after new updates get integrated.

However, while it is reasonably easy to create an extensive plan for comprehensive coverage in POS testing, conducting this type of testing comes with its own set of challenges:

– The diversity in POS hardware and software configurations complicates compatibility testing. Each system may have unique interfaces, requiring thorough validation to ensure seamless integration.

– Data security remains a paramount concern. Payment data encryption, safeguarding against breaches and ensuring compliance with standards like PCI DSS demand rigorous testing protocols.

– The variability in payment methods adds complexity. Testing credit card transactions differs significantly from validating mobile payments or digital wallets, necessitating diverse test scenarios.

– Constant changes in regulations poses a challenge. Adhering to stringent industry regulations and standards, such as EMVCo standards for chip card payments, requires meticulous testing to prevent legal and financial repercussions.

– Additionally, real-world scenarios like network failures or high transaction volumes must be simulated to assess system resilience. These challenges demand comprehensive, scenario-based testing to ensure the reliability, security, and compliance of POS systems, crucial for safeguarding both businesses and customer trust.

Effective POS testing requires a blend of technical expertise and real-world insights. With their extensive experience working with retail enterprises and high-growth firms, in-depth domain expertise and a thorough understanding of local regulations, QE specialists often are a better bet than in-house QA specialists or developers with limited perspectives and experience. The proficiency of seasoned QE experts in simulating varied scenarios, such as network failures or fraudulent activities, often ensures detailed coverage on different tests, thereby aiding application quality and a seamless end-user experience.

QE specialists also bring domain-specific insights, tackling challenges across retail, hospitality, and financial sectors at setups of varying size and business priorities. Their ability to navigate your business context and adapt the POS testing framework for results best suited to your environment makes them a perfect fit for organizations looking to implement POS testing with a minimal learning curve and maximum impact. Partnering with QE specialists also ensures precise, scenario-based testing, guaranteeing a robust and secure POS system tailored to specific business needs.

Software Testing

Software testing involves evaluating applications to ensure they function correctly and meet the desired product and business requirements. It identifies errors and gaps in requirements that may negatively impact the software’s performance, reliability, functionality, or security.

Software testing can be done manually or through automation. Choosing one over the other involves evaluating systemic constraints like budget, testing scope, delivery timelines, efficiency goals, and scalability requirements.

In manual testing, the tester verifies the application like an end user to identify bugs and errors by manually defining and executing the tests. It’s a time-consuming initiative and is often automated in cases where it promotes efficiency, improves accuracy, and enables scalability. However, some instances require the expertise and empathy of manual testers for more effective execution and significant impact. Exploratory or UX testing, which often does not have predefined outcomes to verify, are typical tests that must be carried out manually.

But for many other tests, whose steps and outcomes are pre-defined, need to be repeated multiple times or have neatly organized datasets, they can be automated for a faster, more accurate and consistent testing approach.

Test automation follows a more structured and scalable approach to software testing with a comprehensive framework for consistency, detailed documentation for enhanced visibility and exhaustive reporting with insights and recommendations for further improvement of the testing approach. The testing teams realize the ROI in test automation over a long period of successful implementation and adoption.

While you may notice arguments in favor of test automation quite often, there is a parallel approach that incorporates the best of both manual and test automation to improve product quality without compromising releasing velocity. It is Quality Engineering.

While software testing is all about identifying defects and bugs once software has been built and is ready for release, quality engineering is about ingraining quality into software throughout its development cycle to prevent defects from occurring in it.

Software testing is often a siloed activity that happens after development. In contrast, quality engineering is an endeavor that’s deeply ingrained into SDLC. It involves reimagining product quality right from the design stage with introducing and implementing processes, tools and techstack that help build quality into every step of product development. Quality engineering helps refine quality and makes the process standard and replicable at a large scale.

But quality engineering has been around for some years now. The next revolution in software testing is the advent of Artificial Intelligence (AI). Algorithms can analyze vast datasets, predict potential issues, and automate repetitive testing tasks. This transformative technology not only enhances the speed and efficiency of software testing but also enables testing and developers to delve deeper into complex scenarios, ensuring the robustness and reliability of the software product. AI-driven testing tools can simulate human-like interactions, conduct exhaustive tests, and identify patterns, enabling previously unattainable precision.

Incorporating AI into software testing and quality engineering accelerates the testing and augments the overall quality of software products. By harnessing the power of AI, development teams can proactively address potential issues, optimize their workflows and deliver exceptional, defect-free software to end-users, marking a significant leap forward in the evolution of software development methodologies.

System Upgradation

Upgrading a system involves building an improved version of an existing setup by integrating new features or components for enhanced performance, security and experience. This approach ensures the system remains current and efficient, meeting evolving user expectations and industry standards.
System upgrades happen for one of the following reasons:

  • Technological advancements or changed user preferences make the current setup obsolete
  • Need or demand of newer features, better performance, or enhanced data security
  • Discontinuation of technical support for older versions

While you may have used System update and system upgrade interchangeably in your conversations, they do not mean the same.

System updates happen when minor improvements are integrated into your existing system/software. They may not involve any significant change to your application/software. At the same time, a system/software upgrade happens when a completely newer version of the existing software is developed and released for users. It will not get integrated but will replace the current version when installed.

Like any other software, testing software upgrades is critical to ensure they work seamlessly for existing and new users. Instead of testing the upgraded versions after they have been developed, it’s a more cost-effective and faster approach to simultaneously test them while they are being developed.

Software testing ensures that new features or modifications do not interfere with the existing functionalities. Some of the most common tests conducted are regression testing, compatibility testing and performance testing.

Regression testing validates the entire software to ensure the new changes haven’t inadvertently affected what worked fine earlier.

Whereas compatibility testing checks for newer versions’ browser/OS/device compatibility. Testing across different platforms guarantees a consistent user experience.

Performance testing checks for an upgraded version’s reliability under various loads. This prevents unprecedented slowdowns or crashes during peak usage times.

In essence, software testing for system upgrades is about safeguarding user experience. The meticulous work behind the scenes ensures your favorite applications keep delighting you without a hitch, even after a significant facelift.

However, testing system upgrades for new and existing users involves distinct considerations. For new users, the focus lies on seamless onboarding experiences. Testing should guarantee effortless registration, clear instructions and intuitive interfaces, ensuring a positive first impression and user experience. In contrast, for existing users, the emphasis is on backward compatibility. Rigorous regression testing is vital to confirm that the upgrade keeps their familiar workflow intact.

Additionally, ensuring data integrity during upgrades is crucial for both groups. Testing teams need to validate that existing user data migrates seamlessly. While new users focus on initial impressions, existing users demand consistency and reliability in the upgraded system.

So, next time, you see a notification about a system upgrade, you can be rest assured of a more refined and updated experience of your favorite app.

Test Automation

Software development places paramount importance on efficiency, consistency, scalability and quality. Test automation helps achieve them all.

As technology advances, so does the complexity of applications, making manual testing insufficient and time-consuming. Test automation, therefore, emerges as a game-changer, offering a consistent and efficient way to ensure the quality of software products. Let us delve deeper into its scope, its indispensable role in enabling quality engineering, and whether it’s needed for every organization aiming for faster, high-quality releases.

Test automation involves the use of automated software to execute software testing. By significantly reducing testing time, test automation enables testers to focus on more intricate aspects of software quality. The scope of test automation ranges from unit tests and integration tests to functional and regression tests, covering a wide array of scenarios with steps and outcomes well-defined to ensure comprehensive coverage and rapid feedback.

The most extensive usage of test automation is in quality engineering. It provides swift feedback to developers, helping them rectify issues early in the development cycle. Automating repetitive tests ensures that even the minutest changes are checked consistently, enhancing the reliability of the software being built at every stage. Moreover, it facilitates the creation of a robust test suite, enabling continuous integration and deployment, thereby fostering a culture of collaboration and agility within development teams.

But, success in test automation does not come only by implementing certain tools. The most essential first step is a tailored strategy that aligns with the project’s objectives.

Experienced implementation partners bring valuable insights, expertise and experience to ensure that the automation efforts are optimized per organizational context and hence, unlock maximum benefits sustainably. A robust framework is a guiding light that provides a structured approach to the entire testing journey and enables the reusability of test scripts across cross-functional teams. Not only does it improve testing efficiency, but it also helps teams with ample traceability into STLC. Equally vital is the scalability that accommodates the project’s evolving needs, ensuring the automation framework remains operationally effective and profitable in the long run.

Test automation is an investment that bears fruit from your upcoming release. It drastically reduces the time-to-market by accelerating the testing process without compromising quality.

Catching defects early saves costs associated with fixing issues in later stages of development or, worse, after the product is released. Moreover, enhanced product quality leads to increased customer satisfaction and loyalty. In the competitive landscape of the digital era, organizations that invest in test automation gain a significant advantage, ensuring their products are both high-quality and timely, meeting the ever-growing demands of the market.

Test automation’s ability to streamline testing processes, provide rapid feedback, and enhance overall software quality is unparalleled. To successfully navigate the complexities of today’s software landscape, organizations must embrace test automation and use tailored strategies and trustworthy implementation partners for maximum benefit. By doing so, they pave the way for faster, high-quality releases and delight customers with superior experiences.

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