We no longer hear managers refer to lines of code written, scripts passed, defects identified, etc. as a measure of their testing processes to improve software quality. These measures do not hold any value when it comes to delivering results that matter to your end-users. It does not matter how many defects have been traced in pre-production and realistically speaking, these test metrics do not matter anymore. A QA manager faces great challenges and pressure to test faster and better, and deliver software products with fewer defects. Although it sounds simple, yet managers need to strike the right balance of resources like people, processes, and test management tools.
Not only do managers need to assess the effectiveness of test processes, but also evaluate the performance metrics to identify where problems lie within their processes, in order to meet customer expectations. The long-term success of a QA manager’s strategy depends on measuring change and communicating these factors of change to the team, thus emphasizing using the right metrics and software testing tools. QA managers struggle with finding the right QA metrics, which can lead to frustration between effort and time invested in testing and the results.
In this article, we shall explore five key metrics that QA managers can track to ensure QA teams are headed in the right direction.
Delivering Results as per User Stories
Imagine the frustration when someone made a commitment and failed to deliver in time or meet the standards. This can cause a lot of rework due to which an entire team’s test cycle can be delayed.
Ideally, each user story needs to be delivered at a specific level of QA. As teams continuously plan, commit, and deliver, their ultimate goal should be delivering results with a team’s doneness criteria. Once this can be measured, a manager can be sure about his team’s commitments on schedule and with the highest quality standards.
Active Defect Reporting:
According to QA best practices, a manager should measure the stability of build over time. With the help of this metric, a QA manager can see how many valid defects QA teams have reported, including duplicate, invalid and irreproducible defects. The main aim is to decrease the number of defects identified during each project until a build is stable. However, when a new feature is introduced into an application, it tends to increase the number of defects. QA experts can review the reported defects to identify a pattern of defects and find ways to minimize them.
If a QA team experiences an increase in the number of defects after each build, they could experience any of the following situations:
- QA team tracks multiple issues using one defect or reports new issues while regression testing for the same defect.
- Development teams do not check for defects before delivering a build to the QA team.
- There are communication gaps between onsite and remote QA team members.
Valuing User Sentiments
A QA manager should get to know his end-users by observing how they feel while interacting with an application. By reviewing their feedback regarding the new features, a manager can incorporate their requirements into an upcoming sprint. The least he can do is to develop a plan to deliver a product addressing those requirements. Incorporating user sentiments and feedback can help a firm improve its market presence. This metric covers various aspects of an application including simplicity, stability, performance, usability, and brand image.
Automation: Velocity and Coverage
Automation has gained popularity in almost every industry for its impressive turnaround time to ensure quality software products to market. With respect to software testing, measuring the number of new automation test cases, new automation scripts, and resource allocation allow a QA manager to ensure that their QA teams are sustaining productivity levels.
Better automation coverage not only improves the quality of software applications but also allows manual testers to focus on other areas of QA processes. It is critical for a QA manager to focus on automation coverage by monitoring the total test cases to get a better idea of the pending test cases. This allows the QA manager to steer their teams in the right direction. QA experts have a clear picture of unresolved test cases in modules with less automation coverage.
It is a valuable performance metric as it monitors the speed of test case delivery and identifies which software product needs more attention. This metric helps a manager to ensure:
- Testing systems are stable.
- Automation scripts do not require any updates due to the changing requirements.
- Effective defect reporting.
- The automation team focuses on automated tests instead of functional testing.
Another important metric to improve QA efficiency is keeping track of the defects that are revisited and re-examined from one cycle to the next. This metric highlights the trends of verified, closed, and reopened defects. A QA manager can reduce the number of reopened defects over time. It allows a team to self-organize defects and be accountable for improving the quality of their processes. By allocating the defects and making them visible to all, the team members and stakeholders can see the impact of the decreased number of defects on the application. Teams can also demonstrate how they have delivered results at an increased speed with higher quality and value to the end-user.
Summing It Up
I’m amazed to learn that QA teams are still working the old-fashioned way. QA managers face fierce competition when working with agile teams as achieving quality no longer belongs to a single person. The entire QA team is responsible for delivering results that adhere to quality assurance standards. Thus, a QA manager needs to focus on metrics that really matter, and they need to focus on results that are centered on the value they deliver to their end-users. Meanwhile, teams should not forget how to deliver instead teams should implement the right QA practices using software testing tools while measuring quality via metrics they can use to improve their testing processes over time.