Enhance QA management with department-level metrics
Testing metrics are valuable commodities for quality assurance team leaders and their superiors, providing deep insight into various aspects of the software production process. Metrics can be gathered at every operational level and used to determine the best course of action to take regarding the focus and makeup of a QA team. Some organizations make the mistake of viewing these test management tools through a narrow lens, seeing them as nothing more than assets to enhance current projects. However, testing metrics can go far beyond the project level and offer illuminating data regarding the performance of overall QA efforts for a business. By taking advantage of department-level metrics, QA management and C-level officers can obtain the information required to make necessary course adjustments and improve their software testing practices.
DRE reflects QA effectiveness
One of the most widely used department-level software testing metrics is defect removal efficiency. While viewing raw numbers such as the number of identified bugs or flaws may provide some amount of insight into the performance of a QA team, those figures will not offer a clear window into the overall effectiveness of testing operations. DRE, meanwhile, helps company officials better determine just how well software testers and programmers are at conducting one of their core responsibilities. By comparing the number of corrected flaws with the overall quantity of identified defects, QA management can come to an informed conclusion on the performance of their current team members.
An Infosys white paper explained that DRE can be measured at various moments for different effects. For instance, checking for DRE figures in the midst of development will provide insight into the progress of the current project and how close the software is to becoming a finalized version. On the other hand, organizations can measure DRE metrics after a program has been released to see how their QA teams performed overall. With this information in hand, company executives can make more informed changes to their software testing practices and improve the quality of their products moving forward.
“Calculating DRE post release of a product and analyzing the root causes behind various defects reported by end customers may help strengthen the testing process in order to achieve higher DRE levels for future releases,” the report stated.
Test case distribution by manual and automation
Another valuable metric is measuring the number of manual and automated test cases used over the course of a given project. Although manual testing has its place in QA operations, best practices dictate that automation be leveraged whenever possible. This is particularly true within today’s software development industry as programmers and testers are faced with mounting pressure to get new products out the door as quickly as possible. Meeting accelerated release schedules is no easy task and often results in teams taking short cuts at some point during the production process. Often, software testing is cut back to accommodate short development schedules, resulting in the release of faulty software that requires further patching.
These circumstances frequently occur when QA teams adhere to traditional waterfall methods which typically place testing at the end of the production process. The agile development approach has emerged as a viable alternative to outdated modes of operation, placing more emphasis on comprehensive testing. According to The Register contributor Danny Bradbury, the agile movement has helped highlight the need for automated testing as the process requires QA members to work quickly and respond to changing criteria without delay.
Software development veteran Damian McClellan told Bradbury that test automation tools can be especially beneficial when running regression test cases that ensure legacy features are not disrupted by new additions. This is a critical aspect of software testing as introducing new code into existing programs could easily complicate their processes and affect their overall performance. Regression testing should be run on a regular basis to ensure these types of errors do not become systemic problems. Manually carrying out these processes would be extremely intensive, resulting in a considerable amount of time and energy being spent on them. Automated tools, meanwhile, can execute these vital test cases without too much trouble.
Measuring test case distribution by manual and automation instances will indicate to QA management how efficient their teams are when performing their duties. If they rely too much on manual testing to identify defects and bugs, the development process will likely slow to a crawl. Under these circumstances, software release dates will be pushed back repeatedly, or – more likely – the in-development product will be rushed to completion with flaws firmly intact. By comparing their teams’ use of manual and automation test cases, QA leaders can determine how efficient their software testing operations are and conclude whether any changes need to be made.
The pressures of software development are immense and show no signs of abating anytime soon. QA management should take every opportunity to meet production demands while ensuring they release only the highest-quality programs. Testing metrics offer the insight and guidance needed to properly set up QA teams for success.