Does your SaaS need Software Quality Analytics?

Software quality data doesn’t lie. If your software has bugs or problems, you'll know about it in numerous unwanted ways, including (but not limited to) – customer support tickets, unsubscriptions or uninstalls, and bad reviews or low-star ratings on the app store. These are just some examples of how your customers will respond when your SaaS is not performing in a way that meets their expectations – whether directly or indirectly resulting from bugs or other problems. However, if there was a way to pre-empt these negative results and get real-time data on the progress of several factors (including bugs, crashes, and more) that impact quality, fixing them and ensuring the highest possible quality for your SaaS before release, you can easily delight your users. And, this is why Software Quality Analytics (SQA) is important for SaaS businesses.  To learn more about software quality analytics and its value to SaaS businesses, keep reading!

What is Software Quality Analytics?

Software Quality Analytics or SQA is a process that uses data to measure and improve software quality. The process involves gathering data from multiple sources, analyzing the data, and using it to make decisions to improve software development.

Software quality analytics can be used with any type of software product or service. It's especially useful for products that are delivered over the internet, web, or mobile devices because these products generate and track a lot of customer data. Data can be collected through tools like Google Analytics or custom analytics solutions.

The goal of quality analytics is to help you understand what your customers want, where they're coming from, how they're interacting with your product or service, and which parts of it may need improvement based on their behavior. It helps software teams identify and mitigate risks in their development processes. It provides insights into the quality of your codebase and helps you make informed decisions about how to improve it. These can include:

Code complexity: How many lines of code are in your application?

Cyclomatic complexity: How many branches does each function have?

Maintenance: What percentage of your code is covered by tests?

Test coverage: What percentage of paths through your code are executed during tests?

Security vulnerabilities: How many security vulnerabilities are there in your application?

Why does your SaaS need Software Quality Analytics?

Software Quality Analytics is one of the most effective ways to improve software quality. It is a set of tools that enables you to measure and understand the quality of your software products.

SQA helps you identify, analyze and manage quality issues as they arise. It provides actionable insights into where to focus your efforts and what problems to address first. It helps you avoid costly rework by identifying issues before they become problems for your customers.

If you're wondering why your SaaS needs SQA, here are some reasons:

  • It helps you understand why customers are leaving. When you know why customers are leaving your product, it's easier to come up with ways to retain them.
  • It helps you identify bugs before they become defects or bugs in production environments – saving time and money!
  • It allows teams across different departments (dev, ops, etc.) to collaborate on improving software quality throughout the entire life cycle of a project or product release cycle.

What type of metrics Software Quality Analytics can show?

  • Number of defects per 1000 lines of code (KLOC)
  • Defect density, i.e., number of defects per KLOC
  • Defect prevention rate, i.e., number of defects prevented per million
  • Productivity index: Defects per KLOC divided by hours spent developing the software. This metric is useful for comparing productivity across teams or organizations and for measuring the effectiveness of quality improvement efforts in a given team or organization.
  • Code Quality: The code quality metrics include vocabulary, length, volume, complexity, and the rate of bugs generation in a module. Poor code quality not only reflects bad programming but also makes it tougher to maintain and update the software as it becomes tough for other developers to understand the code.
  • Reliability: The ability of the software to work normally under different circumstances like network/bandwidth/loads is called reliability. This is important because it impacts the performance of the software.
  • Performance: Is the software doing what it is supposed to do? Is it doing it within the time it is supposed to do? How easy/difficult is it to complete the task? And the number of resources it is consuming. All these factors measure the performance of the software. Good software must help the user complete her tasks easily, within the set time, and without consuming too many machine resources.
  • Usability: In an experience economy, usability in general but experience in particular, is the distinctive factor. Usability metrics of the software include onboarding experience, goal accomplishment, bugs, and user-friendliness of the software.
  • Maintenance: Software needs to be upgraded with time to cater to the user’s increasing demands. It also needs to be maintained so that it performs at its peak, all the time. Maintenance metrics include the time/effort required to add new features to the software.
  • Security: How secure is the software? Can it protect against the latest cyber-attack methods? Security is important as most software collects the personal data of the users as well as has access to enterprise data.

How AI Can Aid in Software Quality Analytics?

AI-based software testing tools are gaining popularity because of their ability to automate and scale testing, improve efficiency and accuracy, and reduce costs. AI is being used by several enterprises for various aspects of software quality analytics, including test case generation, bug detection, and defect fixing.

Here are some ways AI can help improve software quality:

  • Automated Testing: Automated tests can be used to ensure that a system behaves as expected at every point during its lifetime. By using AI-based automation testing, organizations can ensure that they have high-quality code across their development lifecycle by continuously monitoring their source code repositories and running automated tests whenever new changes are made.
  • Test Case Generation: Test cases are important for ensuring that the product you build meets your customer expectations. Test case generation automates the process of creating test cases based on user stories or other requirements specifications so engineers can focus on developing products rather than creating test cases manually.
  • Bug Detection: AI-based tools can analyze vast amounts of data to detect bugs quickly and accurately without requiring manual intervention from developers or testers. These bug detection tools typically use machine learning algorithms to scan through codebases looking for bugs.
  • Code analysis: One of the problems with traditional code analysis tools is that they require human intervention to determine if a piece of code needs fixing or not. This means they are expensive to use and have limited scalability because humans cannot keep up with the pace at which new code is being added every day. AI-based code analysis tools can analyze code on their own without needing human intervention, which makes them much more cost-effective than traditional systems and allows them to scale better as well.
  • Performance monitoring: AI-powered Performance monitoring tools can detect performance issues by tracking system usage and performing root cause analysis on detected problems. They also help identify bottlenecks in applications or systems so that they can be addressed immediately before they become larger problems down the road.

Qualetics – Our AI-Powered Software Quality Analytics

With Qualetics, you can finally put your software's quality under the microscope. Qualetics is an AI-powered software quality analytics platform that helps across the entire SDLC, right from Development to Launch, Usage, and Expansion. It allows you to monitor your software’s quality and user behavior over time and make informed decisions about when you should start testing or refactoring. You can also use it to measure the effectiveness of any changes you make to your codebase.

To know more about how this amazing new tool works, click here.