¿Cuáles son los KPI más importantes para la ingeniería de control de calidad y por qué?

In my experience, the best way to think about test coverage in QA engineering is to view it as a multi-dimensional measure. Beyond just quantifying the percentage of code or features tested, it's crucial to consider the depth and relevance of these tests. Effective test coverage should involve a mix of unit, integration, and system tests, tailored to the software's specific risks and requirements. It's not merely about achieving a high percentage but ensuring that critical functionalities and potential failure points are thoroughly examined.

Test coverage refers to the percentage of code or functionality that is tested by a set of tests. It indicates how thoroughly the software or system has been tested, with 100% coverage being ideal.

Customer Satisfaction is the most important KPI. The fundamental job of a tester is NOT to find bugs. It is to act as customer champion and ensure that UX is maintained at a very high level across releases. for this, we should develop KPIs around 2 areas, a. how reliable and consistent is your application across a period of time, like quarter, half-yearly etc b. how attuned is the Engg team towards UX? What is the aging for UX labeled tickets VS regular bugs and features? This will help organization build an engg team that holistically looks at Customer experience and enrich it.

For instance, in a banking software project, we achieved around 85% test coverage, including unit and system tests, which significantly reduced post-deployment bugs. However, we learned that high coverage doesn't always equate to high effectiveness; some critical edge cases were initially missed despite the high coverage.

For instance, in a banking software project, we achieved around 85% test coverage, including unit and system tests, which significantly reduced post-deployment bugs. However, we learned that high coverage doesn't always equate to high effectiveness; some critical edge cases were initially missed despite the high coverage.

What forms the underlying principles of quality engineering? As we delve deeper into this question, we come to the realization that successfully testing a feature is only three fourths of the overall job, predominantly executed by front-line individuals. However, there are additional factors that play a crucial role in promoting quality. These include effective communication, implementing appropriate test frameworks, possessing strong skill sets, asking the right questions, making honest decisions, and upholding moral conduct. Taken together, these elements contribute to the overall success of quality engineering practices and hence be treated as the KPI as well.

It's a vital aspect of QA engineering as it helps identify the extent to which testing has covered various cases. However, its effectiveness relies on alignment with the QA team's business, technical, and analytical approaches to ensure comprehensive coverage.

In my experience, tracking defect density depends heavily on the software development team's reporting accuracy. Inconsistent or incomplete defect documentation can lead to misleading defect density figures, making it a less reliable KPI compared to others. This highlights the need for robust communication and documentation practices in the development team to ensure defect density is a meaningful measure of software quality. While I believe it is helpful, it is remarkably hard to do well.

Defect density refers to the number of defects found in a software product or system, typically measured per thousand lines of code. It helps assess the quality and reliability of the software.

In a recent e-commerce application, we observed a high defect density in the checkout module during peak sale seasons, indicating the need for more focused testing and optimization in that area to enhance user experience and reduce cart abandonment rates.

In a recent e-commerce application, we observed a high defect density in the checkout module during peak sale seasons, indicating the need for more focused testing and optimization in that area to enhance user experience and reduce cart abandonment rates.

Defect density is a crucial Key Performance Indicator (KPI) for QA engineering as it provides a quantitative measure of the quality of software releases. This metric, calculated by dividing the number of defects by the size or volume of the tested code, reflects the concentration of defects per unit of code. A lower defect density indicates a higher level of software quality and effectiveness in identifying and fixing issues during the testing process. By closely monitoring and managing defect density, QA teams can gauge the efficiency of their testing efforts, make data-driven decisions, and continuously enhance software quality throughout the development lifecycle.

To reduce defect density, organizations should adopt a multifaceted approach that combines proactive measures and continuous improvement practices. First and foremost: •Emphasize early defect detection to prevent issues from escalating, reducing the likelihood of higher defect in later stages. •Conduct through root cause analysis for defects to address underlying issues rather than just fixing symptoms, preventing recurring problems. •Regularly monitor and analyze defect density trends throughout the development process to identify patterns and proactively address potential quality issues. •Invest in automated testing to increase coverage and efficiency, reducing the manual effort and minimizing the risk of defects.

Setting KPIs solely for individuals can pose challenges. Metrics like defect resolution time can be problematic as they heavily rely on collective team efforts. If the dev and product teams decide against fixing an issue due to differing priorities, an individual's success in resolving it becomes ambiguous. Instead, effective personal KPIs should be within an individual's control and not contingent on external factors. It's a best practice to focus on team-oriented KPIs that drive results collaboratively. Metrics like defect resolution time are more meaningful when applied to a team consisting of dev and product, as they align with collective goals and responsibilities.

When trying to measure this KPI, its important to consider what type of defects to use to measure these KPIs. There might be issues(defects) which are of low priority and the team considers putting them in the backlog and fixing it at a later stage. considering the resolution time for these defects which are deprioritised might result in a wrong measure. So while defect resolution time/ lead time for fixing a defect can be a good KPI to measure, it should be defined carefully on how and what is being measured.

One other thing that could increase this, is not having a clear view of the issue with concise description and steps to replicate - A sophisticated issue. Sometimes issues are raised, which are vague and not clear. Hence, the development team needs to spend more time to resolve the issue and as a result: the Defect resolution time KPI will increase. And this does not essentially mean that a we are facing an ineffective process.

I'm usually against strict QA KPIs, one of the most laughable are number of defects detected, test cases written or automation vs. manual coverage. However, measuring defect resolution time is a key point, especially for critical issues. It shows how agile actually the team is in reacting. Finding this information in Jira actually costs a lot(in frustration and subscription/plugin costs), so I've build a node.js time measurer to get that information out easily.

On a mobile application project, reducing the average defect resolution time from 48 hours to 24 hours led to quicker updates and improved customer satisfaction, highlighting the importance of a swift response to software issues.

As the primary KPI, I track defect resolution time - the lower the better. Long-standing issues accumulate, delaying productivity and putting consumer trust at risk. I examine lead times for detecting, duplicating, diagnosing, and fixing defects across the development cycle. This highlights the challenges that engineering teams confront when validating mechanical integrity. I also keep track of downtime caused by false rejects by our QA systems. Finally, warranty claims demonstrate whether any faulty components are still delivered to clients. Correlating these KPIs identifies areas for improvement in our verification processes.Catching errors earlier and reducing revork lowers costs while bringing innovative goods to market faster.

Defect resolution time refers to the duration it takes to fully address and rectify a defect or issue. It varies depending on the complexity and severity of the problem, as well as the resources and expertise available. Typically, defect resolution time can range from a few hours to several days, but it ultimately depends on the specific circumstances and the promptness of the resolution process.

The test automation rate in QA can vary depending on various factors such as the complexity of the system, the availability of testing resources, and the organization's testing strategy. However, in general, a reasonable target for test automation is around 70-80% of the regression test suite, with the remaining tests being manual or exploratory testing.

While a high test automation rate offers advantages, pursuing 100% automation carries risks. Not all tests are suited for automation due to complexity or frequent changes. Striving for 100% may lead to unnecessary investment, as some scenarios are more efficiently handled manually. It's vital to prioritize critical and repetitive tests for automation, maintaining a balanced approach to ensure a robust and cost-effective quality assurance process.

From my experience, focusing solely on the test automation rate as a measure of QA efficiency can be misleading. The true effectiveness of test automation lies in the type of tests automated. For instance, a high rate of automation in unit testing is valuable, but if integration or end-to-end tests are neglected, significant issues might go undetected. Therefore, it's crucial to evaluate the balance and distribution of automated tests across different levels to ensure a comprehensive and effective testing strategy.

In an agile development environment for a SaaS product, increasing our test automation rate from 50% to 75% allowed us to maintain release cycles of two weeks, ensuring timely delivery of features without compromising on quality.

In an agile development environment for a SaaS product, increasing our test automation rate from 50% to 75% allowed us to maintain release cycles of two weeks, ensuring timely delivery of features without compromising on quality.

Customer satisfaction is the goal for QA..low customer satisfaction directly impact your customer relationships as well as implies the quality of work done.

After launching a new feature in a travel booking app, we used customer feedback and ratings to gauge satisfaction. High ratings and positive comments indicated that our QA practices were effective in delivering a user-friendly and reliable feature.

Every business should consider this as first priority and as QA we are not supposed to do just testing we should also focus what will be the best approach for any specific UI design till functionality purpose and propose customers a solution and make sure there will be a transparency in the communication so you can understand the requirements, also try to not do the miss commitment this will surely help to rise the customer satisfaction level and also it helps to create perfect bonding between you and your customer.

Based on my experience working with various companies, customer satisfaction is an often overlooked yet critical aspect of QA engineering. In defining quality, it's imperative to focus on whether the customer perceives the software as exceptional, including aspects beyond basic functionality, such as user experience and interface design. Quality assurance should extend to pushing back against poor UX/UI design choices, as these greatly influence customer satisfaction. Too often, QA is narrowly focused on functional testing, while overlooking the broader perspective of what truly satisfies and delights customers.

ROI as metric is key for business decision makers to understand the impact of invested done on Quality. Generally, one of the approach which had been quite effective in getting ROI = ((Gains from Quality- Costs of Quality)/(Costs of Quality))*100% Gains from Quality can be based on costs saved on defects identified early in dev cycle, manual efforts saved based on automation runs etc. Cost of Quality can be costs incurred on resources, tools, licenses etc. over a period of time. This gives a quantitative data for stakeholders. A positive ROI is indicator for an effective Quality Assurance & Engineering approach. It is recommended to calculate ROI at frequent intervals. Quarterly tracking is preferable.

In my experience, companies have not explicitly stated how QA engineering is leading to a clear definition of success and how the different activities align with it. Companies often struggle to connect the dots between QA activities and their overall business goals. This makes it difficult to effectively measure the ROI of QA engineering. It’s crucial for organizations to define their success metrics and understand how QA contributes to these goals, whether through product quality, customer satisfaction, or market positioning, to truly appreciate the ROI of their QA efforts.

Calculating the return on investment (ROI) for QA engineering can be challenging due to various factors. However, studies have shown that investing in QA engineering can result in significant cost savings. One study found that implementing effective QA practices reduced the cost of fixing defects by up to 25%. Additionally, QA engineering helps improve customer satisfaction, reduce software failures, and minimize legal risks. Overall, while the exact ROI may vary, investing in QA engineering is likely to yield substantial returns.

For a cloud storage service, by investing in advanced QA tools and practices, we observed a reduction in downtime incidents, leading to increased user retention and higher revenue, thereby showcasing a substantial return on our QA investments.

It's important to note that calculating ROI in QA is not always straightforward, as some benefits are intangible and challenging to quantify. However, by considering some of the key aspects regarding QA engineering ROI, organizations can make informed decisions about their QA investments and continually improve their software development processes.

Its a simple science, ROI of every business is depends on how you can utilize your resources on a project/product and how much you are investing on them, if the companies are not always relying on cost saving everywhere then can enhance the ROI by investing on advance QA tools and frameworks that can overall increase productivity and efficiency of QA and helps the organization for better ROI.

One valuable personal KPI could be speed. The ability to deliver efficiently is crucial in today's competitive landscape. While a product may be well-qualified, delayed releases can lead to losing market share. Speed as a personal KPI encourages engineers to explore creative solutions, streamline workflows, and contribute to faster delivery, which ultimately benefits the entire team and organization

Another key value to consider is the "Task Switching Rate". During QA process there are times which QAs stuck with a ticket for many different reasons (Back-End, APIs, waiting for and individual to respond and etc.). And one thing which is crucially important is the ability to bring your mind from one subject (issue scenario, user story or else) to a whole different issue/ticket and be able to continue the QA process. This increases the completing rate on features and raised tickets on QA list.

Think of KPIs as the secret sauce for evaluating and boosting the quality of QA engineering. Here are some KPIs that weren't on the list: 1. Mean Time to Detect (MTTD): Checks how quickly the QA team catches and deals with issues. It's like a speedometer for problem-solving. 2. Mean Time to Resolve (MTTR): A measure of how fast we fix issues and get things back on track and a guide for continuous improvement. 3. Defect Leakage: This metric is like a gatekeeper, checking how good our testing is at keeping pesky defects away from the production environment. By keeping an eye on these KPIs, our QA engineering teams can uncover valuable insights, spot areas that need a touch-up, and overall, improve the quality of our software products.

The reality is, many KPIs end up as mere checkboxes, diverting attention from the true goal - exceptional product quality. Engineers find themselves crafting KPIs for the sake of metrics rather than contributing to a robust QA process. Let's move the conversation: What KPIs genuinely measure and enhance product quality, and how can we realign them with our core QA objectives?

Investing in preventive measures, such as code reviews, static analysis, and best coding practices, can reduce the number of defects early in the development process. Also, identifying and fixing defects during the development phase is usually more cost-effective than addressing them in later stages or production.

Few of the additional key KPI's could be: Defect Discovery Trend: Tracks the rate at which defects are found over time. A decreasing trend over time is generally favorable, indicating improving quality. For example, tracking this KPI across sprints can show if quality is improving. Automated Test Proportion: The ratio of automated tests to total tests. A higher ratio is often desirable as it indicates efficiency and reliability in the testing process. For instance, increasing this ratio could be a goal for a team looking to reduce manual testing efforts.

It's also crucial to consider the adaptability of your QA process in rapidly changing environments. For example, in a fast-paced startup, we needed to adjust our QA strategies frequently to align with shifting user needs and technological advancements, emphasizing the importance of flexibility in QA engineering.