Key Aspects Pertaining to QA/QC in the Software Development Process

Mukund Wangikar holding a master’s degree in Computer Management from Savitribai Phule Pune University, Mukund is a passionate technology professional with over 23 years of experiences in Software Product/Platform Management Delivery, Quality Engineering, RPA, Hyper Automation, and UI/UX areas. Prior to InfoVision, he has held key roles across various companies such as UST Xpanxion, Zentest Software, ICERTIS, Symphony Services, IDeaS and Persistent System, to name a few.

In a conversation with Charulatha, a correspondent in Siliconindia magazine, Mukund discusses statistical methods and quality control techniques in software development, emphasizing effective communication of insights to non-technical stakeholders.

Defect tracking & root-cause analysis helps companies to have enhanced level of traceability and recognize areas of product development that are more prone for any mistakes to occur

What are some of the quality control techniques for enhancing the efficiency of the software development process?

Software quality assurance is one of the key aspects of the software development lifecycle. When it comes to product development, platform development or any service improvement, you need implement various methods that will ensure the end quality. For this, companies must first do a careful market analysis, identify customers' expectation, compare it with the pain points that your product/service promised to solve, and then get a clear picture of ways in which your product is beneficial for the customer. Post this, companies must strategies ways in which they can expedite the product using the current trends, test automation being one of the most mature technique of all. Test automation significantly cuts-down the time required to complete the entire product development cycle and enables companies to achieve faster time-to-market.

Another effective strategy that companies can adopt to improve their product development process is continuous integration and continuous deployment along with continuous testing, which enables companies to get rid of any errors at the initial phase of the development cycle itself and save significantly in terms of time, money and other critical resources. Also, defect tracking and root-cause analysis will also help companies to have enhanced level of traceability, recognize areas of the product development cycle that are more prone/vulnerable for any mistakes to occur, and focus their testing efforts more on those areas.

In the realm of software development, Quality Assurance (QA) is a critical component that begins with a deep understanding of customer needs. This involves aligning the product's unique features with the expectations and pain points of its intended users. A clear grasp of how the product benefits the consumer lays the groundwork for an efficient and targeted development process. Test automation plays a pivotal role in this phase, significantly speeding up the development cycle while maintaining the integrity and quality of the software. This method not only accelerates the time-to-market but also ensures a consistent and reliable end product, which is essential in today’s competitive landscape.

Further enhancing the development process, the integration of continuous integration, deployment, and testing is indispensable. This strategy allows for early detection and resolution of errors, which is crucial in minimizing delays and resource expenditure. By addressing issues at the outset, it streamlines the development process, making it more agile and responsive to change. Additionally, a strong focus on defect tracking and root-cause analysis helps in pinpointing specific areas in the development cycle that are prone to errors. Concentrating efforts on these areas ensures that the final software product is not only functional but also robust and reliable. Together, these techniques form a comprehensive approach that not only boosts efficiency but also elevates the overall quality of the software product.

Suggest a few statistical methods for analyzing quality data to identify trends or patterns

In any software testing scenario, there are three kinds of testing assets that you have - your requirements, your test cases, and your defects. To ensure a proper analysis of the entire process, companies must do so in three phases. However, if a product is already launched into the market, the company can refer to social media platforms to check the customer reviews that the product has garnered, which will surely include the defects, if any. By combining this open public feedback on social media about the defects along with the defects that the internal team has identified, the company must do a thorough defect analysis, identify the defect density and then concentrate first on the crucial areas that need immediate rectification. Once your product gets matured over a period of time, you will have a lot of valid test cases added to your database. However, it is difficult and very time consuming to execute all the test cases together when you have time crunch and must do a limited regression. In such cases, I highly recommend the companies to apply Risk Based Testing (RBD) model which will work on two specific parameters - frequency of that particular module to be utilized by the end user and its severity. Based on this, companies can then easily identify their business priorities and act accordingly.

When it comes to analyzing quality data in software testing, think of it like piecing together a puzzle. You have three key pieces: your requirements, your test cases, and your defects. To get the full picture, it's important to look at these in three phases.

Now, let's say your product's already out there in the market. A great way to get real-time feedback is through social media. Customers are pretty vocal about what they love and what bugs them (literally, in terms of software!). This public feedback, combined with what your internal team has found, gives you a goldmine of information. By doing a deep dive into this data, you can figure out the defect density – basically, where the major problem areas are – and focus on fixing these first.

As your product evolves and you accumulate a bunch of test cases, you might find yourself in a bit of a jam when time is short and you can't test everything. That's where Risk-Based Testing (RBT) comes to the rescue. Think of RBT as a smart filter. It helps you prioritize test cases based on two things: how often a part of the software is used and how critical it is. This way, you can zero in on what matters most to your users and what could have the biggest impact if it goes wrong. By aligning your testing efforts with your business priorities, you make sure you're always delivering the best to your users.

Throw some light on the key challenges organizations face while transitioning from a QC-focused mindset to a more data-driven Quality Intelligence approach

The biggest challenge in this regard is the paradigm shift from a traditional QC model towards a newer QA model. In the traditional QC model, there is a click-through testing added at the surface level, which is just one single piece of the overall testing process. Thus, the tester has to play the role of a proxy end user, identify the personas and then write the test cases accordingly. This cultural shift is very important aspect for every tester to meet every challenge in this area. Also, since there are less testers and more automation engineers, these engineers must change their hats and become automation testers and acquire the necessary testing skills. Furthermore, the testing process currently happens based on the tester’s discretion of data, where in most cases they do not have adequate availability of testing data. As a result, procuring this test data is a major challenge that we are most commonly seeing in the market. Other primary challenges in this area include heterogeneous application handling, deciding which kind of metrics to show to which kind of customer, reluctance towards automation, and many others.

Transitioning from a traditional Quality Control (QC) approach to a more advanced Quality Intelligence (QI) model is like navigating a major cultural shift in the world of software testing. The biggest hurdle? It's moving away from the conventional QC mindset, where testing is often a superficial, click-through process. In this old-school method, testers act as stand-ins for the end-users, creating personas and crafting test cases accordingly. This approach is just one piece of the puzzle and doesn't encompass the holistic view that QI demands.

One of the critical changes in this shift is the evolving role of testers. As we see a dip in the number of traditional testers and a rise in automation engineers, these professionals need to swap hats. They're not just engineers anymore; they have to morph into automation testers with a robust set of testing skills. This is a significant cultural and skill-based transformation.

Another challenge lies in the testing process itself. Traditionally, it's been governed largely by the tester's discretion and their access to data, which, more often than not, isn't adequate. Getting hands on the right testing data has become a major stumbling block in the industry. Testers need ample and relevant data to make informed decisions, and that's not always available.

And the challenges don't stop there. We're also looking at issues like handling diverse applications, figuring out what metrics matter to different customers, and overcoming a general reluctance towards automation. Each of these represents a significant shift in approach and mindset, turning the move from QC to QI into a journey that requires time, effort, and a whole lot of strategic thinking.

"In the realm of software development, Quality Assurance (QA) is a critical component that begins with a deep understanding of customer needs"

How can testing teams communicate quality insights and recommendations to non-technical stakeholders?

To convince non-technical personnel regarding the quality of any product or solution, the test manager must be a very good storyteller to clearly convey about the facts that they have tested. Additionally, they must give a clear narrative of the testing process in terms of the purpose of testing it and the outcomes that can be derived from it. Also, the narrative being presented must be data-driven and have a clear visual representation of the entire scenario that is being put-forth. Lastly, whenever the testing process is complete, the testing team can have an executive briefing session where all the key points are mentioned in terms of the journey of the product quality right from inception to delivery, and its current status.

Effective communication of quality insights to non-technical stakeholders hinges on the test manager's ability to be a compelling storyteller. It's about succinctly explaining the purpose and outcomes of the testing process, making it relatable and understandable. The narrative should not just be data-centric but also visually engaging, helping stakeholders easily grasp complex information.

In addition, conducting an executive briefing session post-testing is crucial. This is where the testing team can concisely summarize the journey of the product’s quality from start to finish and its current status. Such sessions offer a clear snapshot of the testing outcomes and open up avenues for questions and feedback, bridging the technical and non-technical divide.